{"session":{"id":"whispering-xylocarp","working_directory":"/Users/root1/Downloads/utilities/benchmarks-prompts","backend_type":"Windsurf","model":"glm-5-2","agent_mode":"bypass","created_at":"2026-06-23T05:26:20+00:00","last_activity_at":"2026-06-23T05:32:16+00:00","title":"GLM 5.2 FP4 Inference on Modal B200s with vLLM","main_chain_id":82,"shell_last_seen_index":0,"cogs_json":[{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/profile"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":[{"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file so that you get the latest version.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nUse your provided search tools instead of `rg`, `grep`, or `find` whenever possible.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright\"},{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"normal"},"created_at":"2026-06-23T05:31:05.205881Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"46ee2cce-51e9-4f95-97d2-6836d610259b"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"glm-5-2","source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/model"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":{"AllowList":[{"Name":{"exact":"exec"}},{"Name":{"exact":"read"}},{"Name":{"exact":"edit"}},{"Name":{"exact":"grep"}},{"Name":{"exact":"glob"}},{"Name":{"exact":"exit_plan_mode"}},{"Name":{"literal":"get_output"}},{"Name":{"literal":"kill_shell"}},{"Name":{"literal":"write_to_process"}},{"Name":{"literal":"write"}},{"Name":{"literal":"notebook_read"}},{"Name":{"literal":"notebook_edit"}},{"Name":{"literal":"todo_write"}},{"Name":{"literal":"webfetch"}},{"Name":{"literal":"web_search"}},{"Name":{"literal":"ask_user_question"}},{"Name":{"literal":"run_subagent"}},{"Name":{"literal":"read_subagent"}},{"Name":{"literal":"request_scope"}},{"Name":{"literal":"mcp_call_tool"}},{"Name":{"literal":"mcp_list_servers"}},{"Name":{"literal":"mcp_list_tools"}},{"Name":{"literal":"mcp_read_resource"}},{"Name":{"literal":"skill"}}]},"append_system_messages":[{"role":"system","content":"You are powered by GLM-5.2.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-06-23T05:31:05.206120Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"c27057ce-82bc-446e-abc9-8f9310253546"}]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/parallel-tool-calls"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Subagent","context":[],"lifetime":{"Unique":"core/read_only_kinds"},"permissions":[[{"Tool":{"Kind":"read"}},"Allow"],[{"Tool":{"Kind":"search"}},"Allow"],[{"Tool":{"Kind":"think"}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/subagent_profiles"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"subagent_profiles"},"created_at":"2026-06-23T05:31:05.206140Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"91ffc4f7-286f-416b-b4de-9a513a345bea"}]},{"model":null,"source":"Managed","context":[],"lifetime":{"Unique":"workspace/config_protection"},"permissions":[[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/cli/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/Downloads/utilities/benchmarks-prompts/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/Downloads/utilities/benchmarks-prompts/.cognition/**/config*.json"}}},"ForceAsk"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Base","context":[],"lifetime":{"Unique":"workspace/read_permissions"},"permissions":[[{"Scope":{"Read":{"glob":"/Users/root1/Downloads/utilities/benchmarks-prompts"}}},"Allow"],[{"Scope":{"Read":{"glob":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501"}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"UserOverride","context":[],"lifetime":{"Unique":"workspace/project_override_permissions"},"permissions":[[{"Scope":{"Command":{"matcher":{"Prefix":"brightness"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"osascript"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"pkill"}}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"workspace/context"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/accept_edits"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/bypass"},"permissions":[["AnyScope","Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/autonomous"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]}],"workspace_dirs":null,"hidden":false,"metadata":"{\"total_credit_cost\":0,\"total_acu_cost\":0.0}"},"message_count":51,"messages":[{"message_id":"d30f5dec-9fd9-48d6-964d-0fd4234734c3","node_id":16,"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file so that you get the latest version.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nUse your provided search tools instead of `rg`, `grep`, or `find` whenever possible.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright\"},{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","created_at":"2026-06-23T05:25:13.400685Z","finish_reason":null,"metrics":null},{"message_id":"74eee7bd-8a4b-4f2b-990c-fdd7b887067e","node_id":17,"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","created_at":"2026-06-23T05:25:13.400948Z","finish_reason":null,"metrics":null},{"message_id":"2f863745-d26b-4fc6-8b48-8625b05dffe5","node_id":18,"role":"system","content":"You are powered by GLM-5.2.","created_at":"2026-06-23T05:25:13.400927Z","finish_reason":null,"metrics":null},{"message_id":"0e8b7a4f-0575-4205-b264-a4759ed64521","node_id":19,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1/Downloads/utilities/benchmarks-prompts (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Monday, 2026-06-22\n\n","created_at":"2026-06-23T05:25:13.008353Z","finish_reason":null,"metrics":null},{"message_id":"066780a8-4676-4553-be11-dfde7e900138","node_id":20,"role":"system","content":"\n\n\n\n","created_at":"2026-06-23T05:25:13.401283Z","finish_reason":null,"metrics":null},{"message_id":"0ff632c7-13bf-4ec5-bbc3-acf7bc6cc1e5","node_id":21,"role":"user","content":"basically i want to run inferenc with glm 5.2 open weights fp4 on modal 8 b200s, what tools will i need, assuming vllm and other things","created_at":"2026-06-23T05:26:20.888658Z","finish_reason":null,"metrics":null},{"message_id":"1cb11f2a-8208-4c56-96bc-c0cbfa109bd6","node_id":22,"role":"system","content":"\nThe following skills can be invoked using the `skill` tool. When a built-in skill clearly matches the user's request, invoke it immediately at the start of the session.\n\n- **devin-for-terminal**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/2026.7.23/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n","created_at":"2026-06-23T05:26:20.889061Z","finish_reason":null,"metrics":null},{"message_id":"d9a94ecd-0f60-4627-92be-562bc88c8a54","node_id":23,"role":"assistant","content":"","tool_calls":[],"thinking":"The user wants to run inference with GLM 5.2 open weights in FP4 quantization on Modal with","created_at":"2026-06-23T05:26:29.119904Z","finish_reason":null,"metrics":{"ttft_ms":2549,"total_time_ms":8213,"input_tokens":null,"output_tokens":0,"cache_read_tokens":null,"cache_creation_tokens":null,"tpot_ms":0,"tokens_per_sec":0}},{"message_id":"c2d6aa04-8e21-4eab-99bd-ba8995555970","node_id":24,"role":"user","content":"basically i want to run inferenc with glm 5.2 open weights fp4 on modal 8 b200s, what tools will i need, assuming vllm and other things, look at the instruction.md files from deep-swe, frontier-swe, how would they write out this prompt, do as much research beforehand as possible","created_at":"2026-06-23T05:26:57.384661Z","finish_reason":null,"metrics":null},{"message_id":"05b2a746-38f4-42df-bffd-994fee508fe8","node_id":26,"role":"assistant","content":"I'll research this in parallel: explore the workspace for the instruction.md files from deep-swe/frontier-swe, and search the web for GLM 5.2 FP4 inference requirements + Modal B200 setup.","tool_calls":[{"id":"chatcmpl-tool-b5b4b784848a91e0","name":"exec","arguments":{"command":"ls -la /Users/root1/Downloads/utilities/benchmarks-prompts && find /Users/root1/Downloads/utilities/benchmarks-prompts -name \"instruction*.md\" -o -name \"INSTRUCTION*.md\" 2>/dev/null | head -50"}},{"id":"chatcmpl-tool-bf6379a3be992bd5","name":"web_search","arguments":{"query":"GLM 5.2 open weights FP4 quantization vllm inference"}},{"id":"chatcmpl-tool-b6b37abdd1d5e755","name":"web_search","arguments":{"query":"Modal B200 GPU deployment vLLM serve"}}],"thinking":"The user wants to:\n1. Run inference with GLM 5.2 open weights FP4 on Modal with 8 B200 GPUs\n2. Know what tools they'll need (vllm and others)\n3. Look at instruction.md files from deep-swe, frontier-swe to see how they'd write out this prompt\n4. Do as much research beforehand as possible\n\nLet me start by exploring the current workspace to find those instruction.md files, and do web research in parallel.","created_at":"2026-06-23T05:27:01.724032Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1540,"total_time_ms":4338,"input_tokens":73,"output_tokens":256,"cache_read_tokens":15017,"cache_creation_tokens":null,"tpot_ms":10.9296875,"tokens_per_sec":91.493924231594}},{"message_id":"62d53c0d-772c-4174-81dd-6f6b9f0ad17c","node_id":27,"role":"tool","content":"Output from command in shell cb737f:\ntotal 0\ndrwxr-xr-x 5 root1 staff 160 Jun 21 23:28 .\ndrwxr-xr-x 7 root1 staff 224 Jun 21 23:28 ..\ndrwxr-xr-x 115 root1 staff 3680 Jun 21 23:28 deep-swe\ndrwxr-xr-x 20 root1 staff 640 Jun 21 23:28 frontier-swe\ndrwxr-xr-x 91 root1 staff 2912 Jun 21 23:28 terminal-bench-2\n\nExit code: 0","created_at":"2026-06-23T05:27:02.702482Z","finish_reason":null,"metrics":null},{"message_id":"10f5f641-57f8-477d-b544-b1619d211638","node_id":28,"role":"tool","content":"# Web Search Results for \"GLM 5.2 open weights FP4 quantization vllm inference\"\n\n## 1. Lorbus/GLM-5.2-NVFP4 · Hugging Face\nURL: https://huggingface.co/Lorbus/GLM-5.2-NVFP4\n\nNVFP4-quantized checkpoint of zai-org/GLM-5.2(753B-param MoE with IndexShare sparse attention). Shrinks the BF16 checkpoint from ~1.37 TB to ~459 GB (≈3× smaller) so it fits on an 8-GPU Blackwell node (e.g. 8×96 GB) with room for long-context KV cache.\n...\nThis is a community-built quantization of GLM-5.2 to NVIDIA's NVFP4 format (E2M1 + FP8 E4M3 scales, 16-element blocks), built using a per-shard streaming recipe derived from NVIDIA ModelOpt's`NVFP4_EXPERTS_ONLY_CFG` and TensorRT-LLM's DeepSeek-V3.2 precision strategy.\n...\n- Base model: GLM-5.2 (753B params, MoE, 78 transformer layers + 1 MTP layer at index 78, IndexShare sparse attention)\n- Quantization: NVFP4 (E2M1 + FP8 E4M3, 16-element block scales)\n- Block size: 16\n- Quant method:`modelopt`\n- Calibration: static per-block percentile-0.9999 scales (no forward-pass calibration — see Limitations)\n- On-disk size: ~459 GB (NVFP4 packed weights + FP8 scales + BF16/FP32 kept layers)\n- Compression: ~1.37 TB (BF16) → ~459 GB ≈ 3.0×\n...\n- Required: NVIDIA Blackwell GPUs (B200, GB200, or RTX PRO 6000 Blackwell). NVFP4 tensor cores are Blackwell-only.\n- VRAM for weights: ~459 GB → minimum 6× 96 GB GPUs just to hold weights; 8 GPUs recommended for KV cache headroom.\n- Tested config: single node, 8× RTX PRO 6000 Blackwell (96 GB each), tensor-parallel 8.\n- Does NOT fit on a single GPU.\n- Inference: TensorRT-LLM, vLLM, or SGLang with`modelopt` NVFP4 support.\n...\n### vLLM (v0.23.0+)\n...\n```\nfrom vllm import LLM, SamplingParams\n\nllm = LLM(\n model=\"Lorbus/GLM-5.2-NVFP4\",\n quantization=\"modelopt\",\n kv_cache_dtype=\"fp8\",\n tensor_parallel_size=8, # needs the full 8-GPU node\n trust_remote_code=True,\n max_model_len=1_000_000,\n)\n\n```\n...\nlaunch_server\n...\nmodel-path Lorbus/GLM-5.2-NVFP4\n...\n--quantization modelopt_fp4 \\\n...\n--kv\n...\ncache-dtype fp8 \\\n...\n8 \\\n...\nload`modelopt` NVFP4\n...\nremote_code\n...\nThis quantization was produced with a per-shard streaming pipeline that downloads GLM-5.2 shards one at a time fr...\n\n## 2. zai-org/GLM-5.2 · Hugging Face\nURL: https://huggingface.co/zai-org/GLM-5.2\n\n0%\n- Pure Open: An MIT open-source license — no regional limits, technical access without borders\n...\nGLM-5.2 supports deployment with the following frameworks. Feel free to try them out:\n...\n- SGLang(v0.5.13.post1+) — see cookbook\n- vLLM(v0.23.0+) — see recipes\n- Transformers(v0.5.12+) — see transformers docs\n- KTransformers(v0.5.12+) — see tutorial\n- Unsloth(v0.1.47-beta+) — see guide\n- For deployment on the`Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.\n...\n## Model tree for zai-org/GLM-5.2\n...\nUse with llama.cpp Use with LM Studio Use with Jan Use with Ollama\n...\n56 models\n\n## 3. Mapika/GLM-5.2-NVFP4 · Hugging Face\nURL: https://huggingface.co/Mapika/GLM-5.2-NVFP4\n\nNVFP4 (4-bit) quantization of zai-org/GLM-5.2, produced with NVIDIA TensorRT Model Optimizer 0.44.0. The MoE expert FFNs (routed + shared) are quantized to NVFP4; attention (MLA + the DeepSeek-style DSA lightning indexer), the router, and the LM head are kept in BF16. This shrinks the checkpoint from 1.5 TB → 410 GB (~3.7×) while retaining GSM8K accuracy within ~2 points of BF16.\n...\nGLM-5.2 is a`glm_moe_dsa` model: DeepSeek-V3.2-style MLA attention + DSA sparse-attention indexer, with a 256-routed-expert + 1-shared-expert MoE (8 experts/token), 78 layers, hidden 6144, vocab 154880.\n...\n## Quantization recipe\n...\n- Format: NVFP4 (FP4 weights + FP8 block scales), block/group size 16,`modelopt` producer.\n- Quantized:`mlp.experts.*`(256 routed experts) and`mlp.shared_experts.*`.\n- Kept in BF16 (excluded): all of`self_attn.*`— MLA projections (q/kv) and the DSA indexer — plus the MoE router (`mlp.gate`) and`lm_head`. The indexer and MLA attention must stay BF16: SGLang's`deepseek_v2` MLA path (used for`glm_moe_dsa`) cannot consume NVFP4 attention weights.\n- KV cache: not quantized.\n- Calibration: 512 samples × 2048 tokens from cnn_dailymail + nvidia/OpenCodeReasoning + nvidia/OpenMathReasoning.\n...\nRequires SGLang ≥ v0.5.13.post1 (the version that registers`GlmMoeDsaForCausalLM`).\n...\n```\ndocker run --runtime=nvidia --gpus '\"device=0,1,2,3\"' --ipc=host --shm-size=32g \\\n -v /path/to/GLM-5.2-NVFP4:/model -p 30000:30000 \\\n lmsysorg/sglang:v0.5.13.post1-cu130 \\\n sglang serve --model-path /model --tp 4 \\\n --quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass \\\n --context-length 32768 --mem-fraction-static 0.85 \\\n --tool-call-parser auto --trust-remote-code --host 0.0.0.0 --port 30000\n\n```\n...\nGPU memory. The weights are ~410 GB, so per-GPU footprint depends on TP:\n...\n| Tensor parallel | Weights / GPU | Suitable GPUs |\n| --- | --- | --- |\n| `--tp 4` | ~110 GB | ≥128 GB cards — H200 (141 GB, tight KV), B200 / B300, MI300X (192 GB) |\n| `--tp 8` | ~55 ...\n\n## 4. amd/GLM-5.2-MXFP4 · Hugging Face\nURL: https://huggingface.co/amd/GLM-5.2-MXFP4\n\namd/GLM-5.2-MXFP4 · Hugging Face\n\n# Model Overview\n\nModel Architecture: GLM-5.2\n\n- Input: Text\n- Output: Text\n\nModel Optimizer: AMD-Quark(V0.11)\n\n- Weight quantization: MOE-only (shared experts quantized), OCP MXFP4, Static\n- Activation quantization: MOE-only, OCP MXFP4, Dynamic\n\nThis model was built with GLM-5.2 model by applying AMD-Quark for MXFP4 quantization.\n\n# Model Quantization\n\nThe model was quantized from zai-org/GLM-5.2 using AMD-Quark. The weights and activations are quantized to MXFP4.\n\nQuantization scripts:\n\n```\ncd Quark/examples/torch/language_modeling/llm_ptq/\n python quantize_quark.py \\\n --model_dir zai-org/GLM-5.2 \\\n --output_dir GLM-5.2-MXFP4 \\\n --quant_scheme mxfp4 \\\n --exclude_layers \"*self_attn*\" \"*mlp.gate\" \"*lm_head\" \\\n \"*mlp.gate_proj\" \"*mlp.up_proj\" \"*mlp.down_proj\" \\\n \"*layers.78.*\" \\ # Exclude the MTP layer (layer 78)\n --file2file_quantization\n\n```\n\n# Deployment\n\n### Use with SGLang/vLLM\n\nThis model can be deployed efficiently using the SGLang or vLLM backends.\n\n## Evaluation\n\nThe model was evaluated on GSM8K benchmarks.\n\n### Accuracy\n\n| Benchmark | GLM-5.2 | GLM-5.2-MXFP4(this model) | Recovery |\n| --- | --- | --- | --- |\n| GSM8K (flexible-extract) | 94.09 | 93.93 | 99.8% |\n\n### Reproduction\n\nThe GSM8K results were obtained using the`lm-evaluation-harness` framework, based on the Docker image`lmsysorg/sglang:v0.5.13.post1-rocm700-mi35x`, with SGLang pre-installed inside the image and lm-eval compiled and installed from source.\n\n```\nlm_eval --model sglang \\\n --model_args pretrained=amd/GLM-5.2-MXFP4,tp_size=4 \\\n --tasks gsm8k \\\n --batch_size auto\n\n```\n\nThe Docker image`rocm/vllm-dev:nightly_main_20260616` with vLLM pre-installed can also be used for reproducing using vLLM backend.\n\n```\nexport VLLM_ROCM_USE_AITER=1\nexport VLLM_ROCM_USE_AITER_FP8BMM=0\nexport VLLM_ROCM_USE_AITER_FP4BMM=0\nlm_eval --model vllm \\\n --model_args 'pretrained=amd/GLM-5.2-MXFP4,tensor_parallel_size=4,dtype=aut...\n\n## 5. lukealonso/GLM-5.2-NVFP4 · Hugging Face\nURL: https://huggingface.co/lukealonso/GLM-5.2-NVFP4\n\nlukealonso/GLM-5.2-NVFP4 · Hugging Face\n\n### Note: Quality checks still in progress\n\n## Model Description\n\nGLM-5.2-NVFP4 is an NVFP4-quantized version of zai-org/GLM-5.2, a 744B-parameter Mixture-of-Experts language model with 40B active parameters, 256 experts per MoE layer (8 activated per token), and DeepSeek Sparse Attention (DSA).\n\nQuantized directly from the full BF16 checkpoint (zai-org/GLM-5.2, not the FP8 release, to NVFP4 (4-bit with blockwise FP8 scales per 16 elements) using NVIDIA Model Optimizer.\n\n### What's quantized\n\nOnly the non-shared MoE expert MLP projections are quantized to NVFP4. Attention weights are left in BF16, in addition to the dense MLPs (layers 0-3) and the shared experts. Since the MoE expert weights constitute the vast majority of model parameters in an MoE architecture, this still yields significant memory savings.\n\nCalibration uses natural top-k routing rather than forcing all experts to activate, so each expert's quantization scales reflect the token distributions it actually sees during inference. To compensate, calibration was run on a much larger number of samples than typical to ensure broad expert coverage through natural routing alone.\n\n### Calibration dataset\n\nThree calibration passes were run:\n\n1. Coding pass — Agentic coding samples (tool calling, multi-turn code generation, function calling) with English and Chinese system prompts.\n2. Broad pass — Large-scale diverse samples drawn from WildChat-NonToxic and LMSYS-Chat covering real user conversations across a wide range of topics and languages.\n3. Deep pass — Long-context samples (>8K tokens) from coding and diverse sources to exercise deep-sequence expert activation patterns.\n\n### Requirements\n\nHardware: 8x RTX PRO 6000 Blackwell 96GB (b12x MoE runner recommended)\n\n### Community Testing\n\nDownloads last month 27,157\n\nSafetensors https://huggingface.co/docs/safetensors\n\nModel size\n\n432B params\n\nTensor type\n\nBF16\n\n·\n\nF8_E4M3\n\n·\n\nU8\n\n·\n\nF32\n\n·\n\nChat template\n\nFiles info\n\nIn...\n","created_at":"2026-06-23T05:27:02.702507Z","finish_reason":null,"metrics":null},{"message_id":"a0d2e72d-13ec-4e94-89bc-fb02724f3ae5","node_id":29,"role":"tool","content":"# Web Search Results for \"Modal B200 GPU deployment vLLM serve\"\n\n## 1. Run OpenAI’s gpt-oss model with vLLM | Modal Docs\nURL: https://modal.com/docs/examples/gpt_oss_inference\n\nvllm_image = (\n modal.Image.from_registry(\n \"nvidia/cuda:12.8.1-devel-ubuntu22.04\",\n add_python=\"3.12\",\n )\n .entrypoint([])\n .uv_pip_install(\n \"vllm==0.18.1\",\n \"huggingface_hub[hf_transfer]==0.36.0\",\n )\n .env( # fast Blackwell-specific MoE kernels\n {\"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8\": \"1\"}\n )\n)\n...\nWe'll be downloading OpenAI's model from Hugging Face. We're running\nthe 20B parameter model by default but you can easily switch to the 120B model,\nwhich also fits in a single H100 or H200 GPU.\n...\n## Build a vLLM engine and serve it\n...\nThe function below spawns a vLLM instance listening at port 8000, serving requests to our model.\n...\n```python\napp = modal.App(\"example-gpt-oss-inference\")\n\nN_GPU = 1\nMINUTES = 60 # seconds\nVLLM_PORT = 8000\n...\n@app.function(\n image=vllm_image,\n gpu=f\"B200:{N_GPU}\",\n scaledown_window=10 * MINUTES, # how long should we stay up with no requests?\n timeout=30 * MINUTES, # how long should we wait for container start?\n volumes={\n \"/root/.cache/huggingface\": hf_cache_vol,\n \"/root/.cache/vllm\": vllm_cache_vol,\n \"/root/.cache/flashinfer\": flashinfer_cache_vol,\n },\n)\n...\n@modal.concurrent(max_inputs=MAX_INPUTS)\n@modal.web_server(port=VLLM_PORT, startup_timeout=30 * MINUTES)\ndef serve():\n import subprocess\n\n cmd = [\n \"vllm\",\n \"serve\",\n MODEL_NAME,\n \"--revision\",\n MODEL_REVISION,\n \"--served-model-name\",\n MODEL_NAME,\n \"llm\",\n \"--host\",\n \"0.0.0.0\",\n \"--port\",\n str(VLLM_PORT),\n \"--async-scheduling\", # reduces host overhead, but might not be compatible with all features\n ]\n\n # enforce-eager disables both Torch compilation and CUDA graph capture\n # default is no-enforce-eager. see the --compilation-config flag for tighter control\n cmd += [\"--enforce-eager\" if FAST_BOOT else \"--no-enforce-eager\"]\n\n # assume multiple GPUs are for splitting up la...\n\n## 2. GPU acceleration | Modal Docs\nURL: https://modal.com/docs/guide/gpu\n\ngpu` argument\n...\n- `T4`\n- `L4`\n- `A10`\n- `L40S`\n- `A100`\n- `A100-40GB`\n- `A100-80GB`\n- `RTX-PRO-6000`\n- `H100`/`H100!`\n- `H200`\n- `B200`/`B200+`\n...\nFor instance, to use a B200, you can use `@app.function(gpu=\"B200\")`.\n...\n200\n...\n1,536 GB GPU RAM\n...\nmore than\n...\nwill usually result in larger wait times. These\nGPUs are always attached to\n...\nsame physical machine\n...\n## B200 GPUs\n...\nModal's most powerful GPUs are the B200s,\nNVIDIA's flagship data center chip for the Blackwell architecture.\n...\nTo request a B200, set the `gpu` argument to `\"B200\"`\n...\n```python\n@app.function(gpu=\"B200:8\")\ndef run_deepseek():\n ...\n\n```\n...\nCheck out this example to see how you can use B200s to max out vLLM serving performance for LLaMA 3.1-8B.\n...\n### Opt-in upgrade to B300\n...\nUse `gpu=\"B200+\"` to allow Modal to run requests on either B200 or B300 GPUs. B200+ is billed as B200, regardless of which GPU is used. Use this option only if your code is compatible with both types of GPUs. B300 requires CUDA version 13.0+. Use this to have access to a greater capacity pool automatically.\n\n## 3. How to deploy vLLM\nURL: https://modal.com/blog/how-to-deploy-vllm\n\nModal handles much of this heavy lifting for you. Modal consists of a Python SDK that wraps an ultra-fast container stack and multi-cloud GPU pool. It abstracts away container management, GPU scheduling, and autoscaling, letting you focus on customizing your model and serving logic.\n...\nModal makes it easy to deploy open-source models on powerful GPUs. If you don’t have a Modal\n...\n, follow the two-line setup instructions\n...\nthe Quickstart\n...\nfollowing this tutorial\n...\nTo serve vLLM, you need a container image—a packaged environment that includes Python, CUDA (NVIDIA’s GPU acceleration toolkit), and all the model dependencies. Modal’s SDK makes image definition easy, since you can define all your requirements in-line with your application code.\n...\ninference: Py\n...\nFlashInfer,\n...\nHugging Face Hub\n...\nenvironment variable speeds up model downloads using Hugging Face’s optimized transfer utility\n...\nvLLM supports JIT compilation (just-in-time kernel optimization\n...\nand CUDA graph capture, both of\n...\nspeed up inference after startup. However, enabling them increases initialization time, otherwise known as a cold start.\n...\ntrade-off.\n...\nHere,`subprocess.Popen` launches`vllm serve` as a background process inside your container. When the model finishes loading, it begins accepting requests.\n...\n@app.\n...\nimage=vllm_image,\n gpu=H100:1,\n scaledown_window=900, # how long should we stay up with no requests?\n timeout=10 * MINUTES, # how long should\n...\nwait for container start?\n volumes={\n \"/root/.cache/huggingface\": hf_cache_vol,\n \"/root/.cache/vllm\": vllm_cache_vol,\n },\n)\n...\n@modal.web_server(port=8000, startup_timeout=10 * MINUTES)\n...\ndef serve():\n import subprocess\n\n cmd = [\n \"vllm\",\n \"serve\",\n \"--uvicorn-log-level=info\",\n MODEL_NAME,\n \"--revision\",\n MODEL_REVISION,\n \"--served-model-name\",\n MODEL_NAME,\n \"llm\",\n \"--host\",\n \"0.0.0.0\",\n \"--port\",...\n\n## 4. Introducing: B200s and H200s on Modal\nURL: https://modal.com/blog/introducing-b200-h200\n\nIt never gets old: we are pleased to announce that the most powerful GPUs on the market are now available on Modal.\n...\nNo “contact sales” button or asking for quota. B200s and H200s are accessible to anyone with a Modal account. It’s a one-liner to add them to any Modal Function.\n...\n```\nimport modal\n\napp = modal.App()\n\n@app.function(gpu=\"B200\") # or \"H200\"\ndef run_big_model():\n # This will run on a Modal B200\n```\n...\nMany workloads will see an immediate speedup between 2x and 4x when migrated from H100s to B200s. But the full performance benefits of B200s in particular will take some time to realize. For example, the popular open source LLM serving engine vLLM just stabilized support for Blackwell GPUs in version 0.9, released on Tuesday. And achieving the peak performance promised by the hardware will take even more work.\n...\nBelow is one popular workload where our benchmarking showed significant gains for latency and throughput on B200s vs H200s. We used the latest version of vLLM to run the DeepSeek V3 Large Mixture-of-Experts Language Model in its native 8-bit precision to process 1000 tokens (about one page of text) as input and generate 128 tokens (e.g. a summary paragraph) as output. This model is too large to run on an 8xH100, but we benchmarked it on an 8xH200 and an 8xB200 on Modal.\n...\n- At 1 request per second, the median time-to-first-token is 2.5x faster on the B200s vs H200s.\n- At a median time-to-first-token of 1 second, queries-per-second is 1.7x higher on the B200s vs H200s.\n...\nYou can run vLLM on Modal B200s with this code sample.\n...\nModal is the easiest way to deploy code to GPUs with no reservations or commitments. Our custom infrastructure allows us to spin up GPU containers running your code in less than a second. We help you efficiently autoscale your workloads to hundreds of GPUs, and you only ever pay for what you use.\n...\nModal also comes with $30/month in free compute, so you can try B200s or H200s for free right now! Sign up today...\n\n## 5. 06_gpu_and_ml/llm-serving/vllm_inference.py at main · modal-labs/modal-examples\nURL: https://github.com/modal-labs/modal-examples/blob/main/06_gpu_and_ml/llm-serving/vllm_inference.py\n\n# In this example, we show how to run a vLLM server in OpenAI-compatible mode on Modal.\n...\norder of business is to define\n...\nwill run in:\n...\n# the [\n...\n`](https://\n...\ndocs/guide/custom-container\n...\n# vLLM can be installed with `uv pip`, since Modal [provides the CUDA drivers](https://modal.com/docs/guide/cuda).\n...\n-devel-\n...\n\",\n \"\n...\n.36.\n...\n\",\n )\n...\n({\"HF_\n...\n_HIGH_\n...\n# Native hardware support for FP8 formats in [Tensor Cores](https://modal.com/gpu-glossary/device-hardware/tensor-core)\n# is limited to the latest [Streaming Multiprocessor architectures](https://modal.com/gpu-glossary/device-hardware/streaming-multiprocessor-architecture),\n# like those of Modal's [Hopper H100/H200 and Blackwell B200 GPUs](https://modal.com/blog/introducing-b200-h200).\n...\n# ## Build a vLLM engine and serve it\n...\n# The function below spawns a vLLM instance listening at port 8000, serving requests to our model.\n# We wrap it in the [`@modal.web_server` decorator](https://modal.com/docs/guide/webhooks#non-asgi-web-servers)\n# to connect it to the Internet.\n\n# The server runs in an independent process, via `subprocess.Popen`, and only starts accepting requests\n# once the model is spun up and the `serve` function returns.\n...\napp = modal.App(\"example-vllm-inference\")\n...\nN_GPU = 1\nMINUTES = 60 # seconds\nVLLM_PORT = 8000\n...\n@app.function(\n image=vllm_image,\n gpu=f\"H100:{N_GPU}\",\n scaledown_window=15 * MINUTES, # how long should we stay up with no requests?\n timeout=10 * MINUTES, # how long should we wait for container start?\n volumes={\n \"/root/.cache/huggingface\": hf_cache_vol,\n \"/root/.cache/vllm\": vllm_cache_vol,\n },\n)\n...\n@modal.web_server(port=VLLM_PORT, startup_timeout=10 * MINUTES)\ndef serve():\n import subprocess\n\n cmd = [\n \"vllm\",\n \"serve\",\n \"--uvicorn-log-level=info\",\n MODEL_NAME,\n \"--revision\",\n MODEL_REVISION,\n \"--served-model-name\",\n MODEL_NAME,\n \"llm\",...\n","created_at":"2026-06-23T05:27:02.702521Z","finish_reason":null,"metrics":null},{"message_id":"d0d7844a-14e5-477e-a66f-bcb9571aca4a","node_id":31,"role":"assistant","content":"Good context. Now let me look at the instruction.md files in deep-swe and frontier-swe to understand their prompt style.","tool_calls":[{"id":"chatcmpl-tool-8cb1a6c3db3d10cd","name":"exec","arguments":{"command":"find /Users/root1/Downloads/utilities/benchmarks-prompts/deep-swe /Users/root1/Downloads/utilities/benchmarks-prompts/frontier-swe -iname \"*instruction*\" -o -iname \"*prompt*\" 2>/dev/null | head -30 && echo \"---\" && ls /Users/root1/Downloads/utilities/benchmarks-prompts/deep-swe | head -30 && echo \"---\" && ls /Users/root1/Downloads/utilities/benchmarks-prompts/frontier-swe | head -30"}}],"thinking":"Good, I have a lot of info. Now let me look at the instruction.md files in deep-swe and frontier-swe to see how they write prompts.","created_at":"2026-06-23T05:27:08.103296Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1075,"total_time_ms":5396,"input_tokens":5850,"output_tokens":179,"cache_read_tokens":15089,"cache_creation_tokens":null,"tpot_ms":24.139664804469273,"tokens_per_sec":41.42559592686878}},{"message_id":"3d4095d8-1063-414f-b6f7-5d4a8a81954e","node_id":32,"role":"tool","content":"Output from command in shell 24ae4b:\n---\nabs-module-cache-flags.md\nabs-stepped-slices.md\nactionlint-action-pinning-lint.md\nadaptix-name-mapping-aliases.md\naiomonitor-task-snapshots-diff.md\nanko-default-function-arguments.md\nanko-typed-variable-bindings.md\narcane-drift-detection-baselines.md\narktype-json-schema-refs-dependencies.md\nawilix-async-container-initialization.md\nbandit-incremental-cache-control.md\nbandit-interprocedural-taint-checks.md\nbandit-structured-nosec-directives.md\nboa-hierarchical-evaluation-cancellation.md\ncattrs-partial-structuring-recovery.md\nclack-async-autocomplete-options.md\nclaude-code-by-agents-recursive-delegation.md\ncliffy-config-file-parsing.md\ncsstree-shorthand-expansion-compression.md\ndasel-html-document-format.md\ndateutil-rfc5545-timezone-interop.md\ndrizzle-orm-window-function-builders.md\ndynamodb-toolbox-conditional-attribute-requirements.md\ndynamodb-toolbox-lazy-recursive-schemas.md\neffect-sse-httpapi-streaming.md\neicrud-keyset-pagination-cursor.md\netree-xml-diff-patch.md\nexpr-try-catch-errors.md\nfastapi-deprecation-response-headers.md\nfastapi-implicit-head-options.md\n---\ncranelift-codegen-opt.md\ndart-style-haskell.md\ndependent-type-checker.md\nffmpeg-swscale-rewrite.md\nfrogsgame-rl.md\ngit-to-zig.md\ngranite-mamba2-inference-optimization.md\ninference-system-optimization.md\nlibexpat-to-x86asm.md\nlua-native-compiler.md\nmodular-stack-wan21.md\nnotebook-compression.md\noptimizer-design.md\npcqm4mv2-autoresearch.md\npostgres-sqlite-wire-adapter.md\npyright-type-checking-optimization.md\nrevideo-perf-opt.md\nworkspace.md\n\n\nExit code: 0","created_at":"2026-06-23T05:27:08.149892Z","finish_reason":null,"metrics":null},{"message_id":"868f39d0-c575-4303-a85f-68758f0caea8","node_id":34,"role":"assistant","content":"Let me look at a few example prompts from each directory to understand the writing style.","tool_calls":[{"id":"chatcmpl-tool-85ef2b8f7e2b2a48","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/frontier-swe/inference-system-optimization.md"}},{"id":"chatcmpl-tool-bd589a8fb86c4579","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/frontier-swe/granite-mamba2-inference-optimization.md"}},{"id":"chatcmpl-tool-b5c883194d7bbd9f","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/frontier-swe/workspace.md"}}],"thinking":"No \"instruction\" files found by name. Let me look at a few example prompts from each directory to understand the style. Let me look at frontier-swe's files since they seem more focused on inference/perf topics, and a couple deep-swe ones.","created_at":"2026-06-23T05:27:13.056684Z","finish_reason":"tool_calls","metrics":{"ttft_ms":912,"total_time_ms":4905,"input_tokens":625,"output_tokens":168,"cache_read_tokens":20938,"cache_creation_tokens":null,"tpot_ms":23.767857142857142,"tokens_per_sec":42.073628850488355}},{"message_id":"b12a35c7-7a69-441d-b681-2e2761adff28","node_id":35,"role":"tool","content":"\n 1|# Inference System Optimization\n 2|\n 3|You have an SGLang serving instance with Qwen3.5-4B on a B200 GPU.\n 4|Your goal is to make it serve requests as fast as possible.\n 5|\n 6|The verifier launches your server using `/app/submission/launch_server.sh`, sends hidden\n 7|requests, and measures end-to-end latency against a **well-tuned SGLang\n 8|baseline**. The baseline already uses FP8 KV cache, native MTP speculative\n 9|decoding, the V2 mamba scheduler, CUDA graphs, and optimal memory allocation.\n 10|To beat it, you need to go beyond configuration — custom kernels, SGLang\n 11|source modifications, or model surgery.\n 12|\n 13|Your score is the geometric-mean speedup across all hidden workloads.\n 14|The verifier tests both sequential single-request latency and concurrent\n 15|batched requests at multiple concurrency levels. Your optimisation must\n 16|handle both efficiently.\n 17|\n 18|A correctness gate runs before speed is measured — your server's outputs\n 19|must match the baseline's outputs on a large set of hidden prompts,\n 20|including adversarial and degenerate inputs.\n 21|\n 22|## Model\n 23|\n 24|- **Qwen/Qwen3.5-4B** — a natively multimodal (text + vision) model,\n 25| 4B parameters, bfloat16.\n 26|- Weights are pre-downloaded at `/app/model`.\n 27|- License: Apache 2.0.\n 28|\n 29|## Files\n 30|\n 31|- `/app/submission/launch_server.sh`\n 32| - **This is the main file you modify.** The verifier executes it to start\n 33| your candidate server. It receives `PORT` and `MODEL_PATH` as environment\n 34| variables.\n 35|- `/app/submission/`\n 36| - This is the owned submission root.\n 37| - `launch_server.sh` and any helper files or copied SGLang/FlashInfer patches\n 38| needed at verification time must live under this tree.\n 39|- `/app/run_dev_bench.py`\n 40| - Public dev benchmark. Launches your server, sends test requests, reports\n 41| latency. The hidden verifier uses different workloads and more iterations.\n 42|- `/app/verify_serving.py`\n 43| - Quick sanity check that the server starts and produces coherent outputs.\n 44|- `/app/optimize.py`\n 45| - Convenience script that runs verify + benchmark in sequence.\n 46|\n 47|## What you can do\n 48|\n 49|Anything. The design space is intentionally wide:\n 50|\n 51|- **Server configuration**: quantisation (fp8, int8, int4), torch.compile,\n 52| CUDA graphs, chunked prefill, memory allocation tuning, batch size limits\n 53|- **Speculative decoding**: self-speculative, draft model, Medusa heads\n 54|- **Custom kernels**: write Triton, TileLang, or CuTe DSL kernels and plug\n 55| them into SGLang\n 56|- **SGLang source modifications**: modify the installed SGLang source directly.\n 57| Find it with: `python3 -c \"import sglang; print(sglang.__path__[0])\"`\n 58|- **Model modifications**: quantise weights, prune layers, fuse operations\n 59|- **Scheduling**: tune the request scheduler, batching strategy, preemption\n 60|\n 61|Pre-installed kernel tools:\n 62|- **CUDA** — native CUDA kernels (full dev toolkit)\n 63|- **Triton** — comes with PyTorch\n 64|- **CuTe DSL / CuTile** — via CUTLASS (`import cutlass`)\n 65|- **FlashInfer** — already powering SGLang's attention; source is modifiable\n 66|- **TileLang** (`import tilelang`) — Python DSL for GPU kernels\n 67|- **ThunderKittens (TK)** (`import thunderkittens`) — Stanford GPU kernel lib\n 68|- **Helion** (`import helion`) — Meta's Python DSL for GPU kernels\n 69|- **Triton-TLX** — Triton extension library\n 70|\n 71|SGLang internals and FlashInfer kernels are straightforward to modify\n 72|directly. Find SGLang source with:\n 73|`python3 -c \"import sglang; print(sglang.__path__[0])\"`\n 74|\n 75|## What has to stay correct\n 76|\n 77|The verifier runs your server and the baseline on the same hidden prompts\n 78|(temperature=0, greedy decoding) and compares outputs at the token level.\n 79|The prompt set includes normal text, degenerate inputs, and adversarial\n 80|edge cases. Your optimisations must preserve the model's output quality\n 81|across all of them — the baseline operates in bfloat16, and your outputs\n 82|are expected to match. If the correctness gate fails, your score is zero\n 83|regardless of speed.\n 84|\n 85|## How to work\n 86|\n 87|Start here:\n 88|\n 89|```bash\n 90|# Quick sanity check\n 91|uv run --no-sync python verify_serving.py\n 92|\n 93|# Full benchmark\n 94|uv run --no-sync python run_dev_bench.py\n 95|\n 96|# Or both in sequence\n 97|uv run --no-sync python optimize.py\n 98|```\n 99|\n100|These scripts launch the server from `/app/submission/launch_server.sh`, run their\n101|checks, and then shut it down. Use `--no-server` if you already have a server\n102|running.\n103|\n104|To iterate quickly, you can start the server manually in one terminal and\n105|benchmark from another:\n106|\n107|```bash\n108|# Terminal 1: start server\n109|PORT=30000 MODEL_PATH=/app/model bash /app/submission/launch_server.sh\n110|\n111|# Terminal 2: benchmark against running server\n112|uv run --no-sync python run_dev_bench.py --no-server --port 30000\n113|```\n114|\n115|## Resource Constraints\n116|\n117|Analyze GPU memory constraints before changing server configuration\n118|(`nvidia-smi`, server logs). SGLang pre-allocates memory for caches,\n119|CUDA graph capture, and runtime buffers — these compete with model\n120|weights for the available HBM. An out-of-memory error will kill the\n121|server process and score zero. Plan your memory allocation strategy\n122|and verify the server starts successfully before committing to a\n123|configuration.\n124|\n125|## Constraints\n126|\n127|You CAN:\n128|\n129|- modify `/app/submission/launch_server.sh` and create any helper files under `/app/submission`\n130|- modify SGLang source code in site-packages\n131|- modify model weights (quantise, prune, etc.)\n132|- use torch.compile, Triton, TileLang, CuTe DSL, custom CUDA kernels\n133|- install additional packages from the pre-built cache (no internet)\n134|\n135|You CANNOT:\n136|\n137|- access or reference `/tests/` or hidden verifier files\n138|- disable the timer daemon\n139|- access the internet at runtime\n140|\n141|## Time Budget\n142|\n143|Your wall-clock budget is enforced by Harbor and exposed through a timer daemon:\n144|\n145|```bash\n146|cat /app/.timer/remaining_secs\n147|cat /app/.timer/elapsed_secs\n148|test -f /app/.timer/alert_30min\n149|test -f /app/.timer/alert_10min\n150|test -f /app/.timer/alert_5min\n151|```\n152|\n153|Keep a working `/app/submission/launch_server.sh` at all times. Leave time for a final\n154|correctness check and benchmark run.\n155|\n156|## Persisted Submission State\n157|\n158|Everything needed to run your submission at verification time must be in\n159|`/app/submission/`.\n160|\n161|Your entrypoint is `/app/submission/launch_server.sh`.\n162|If your final server behavior depends on helper scripts, copied site-packages\n163|patches, custom kernels, or config files, keep them under `/app/submission/`\n164|and have `launch_server.sh` reapply or import them at startup. Direct edits in\n165|site-packages alone may be lost during replay.\n","created_at":"2026-06-23T05:27:13.121059Z","finish_reason":null,"metrics":null},{"message_id":"1f299705-93b3-4101-9233-2496489e2c0d","node_id":36,"role":"tool","content":"\n 1|# Granite Mamba2 Inference Optimization\n 2|\n 3|This task is a standalone port of the real Hugging Face Granite hybrid Mamba2 layer,\n 4|using extracted weights from the pinned checkpoint `ibm-granite/granite-4.0-h-1b-base`.\n 5|\n 6|The provided `reference_impl.py` is a clean port of the HF `GraniteMoeHybridMambaLayer`\n 7|`torch_forward` path. It does not call `transformers` in the actual forward path.\n 8|Your job is to make `/app/submission/candidate_impl.py` faster without changing semantics.\n 9|\n 10|The model runs in **bfloat16** (`torch.bfloat16`) on CUDA. This affects optimization\n 11|choices such as Triton intrinsics and accumulation precision.\n 12|\n 13|The verifier checks correctness against both the pinned `transformers`\n 14|implementation and the fixed task reference, then measures speed relative to\n 15|an optimized baseline that uses production-grade Triton kernels. Your score\n 16|is the geometric-mean paired speedup of your candidate over this baseline.\n 17|A score of 1.0 means matching production speed; >1.0 means beating it.\n 18|\n 19|## Fixed API\n 20|\n 21|The verifier imports `CandidateBlock` from `/app/submission/candidate_impl.py` and calls:\n 22|\n 23|```python\n 24|block = CandidateBlock(weights, config, device=device, dtype=dtype)\n 25|hidden_out, readout_logits, new_cache = block.forward(\n 26| hidden_states,\n 27| cache=None,\n 28| attention_mask=attention_mask,\n 29|)\n 30|```\n 31|\n 32|Keep that constructor and method signature stable.\n 33|\n 34|`readout_logits` is a last-token readout using the real Granite final norm plus tied\n 35|embedding head. It is there for correctness checking, not because the task is a full LM.\n 36|The latency benchmark times the Mamba layer core path (`torch_forward`), not the large\n 37|readout head.\n 38|\n 39|## Files\n 40|\n 41|- `/app/reference_impl.py`\n 42| - Fixed standalone port of the real Granite Mamba layer.\n 43|- `/app/vllm_ops/`\n 44| - Optimized Triton kernel implementations for SSM scan, state passing,\n 45| and related operations. These are building blocks you can use in your\n 46| candidate implementation.\n 47|- `/app/submission/candidate_impl.py`\n 48| - Your implementation entrypoint.\n 49|- `/app/submission/`\n 50| - This is the owned submission root.\n 51| - `candidate_impl.py` and any helper files or config it needs at verification\n 52| time must live under this tree.\n 53|- `/app/task_fixtures.py`\n 54| - Fixed utilities: asset loading, cache structure, public workloads, tensor comparisons,\n 55| and the bridge to `transformers`.\n 56|- `/app/prepare_assets.py`\n 57| - Build-time extractor for the pinned Granite checkpoint slice.\n 58|- `/app/verify_api.py`\n 59| - Public parity check against both the reference and `transformers`.\n 60|- `/app/run_dev_bench.py`\n 61| - Public latency benchmark on visible workloads, timing the core layer path\n 62| against the provided baseline.\n 63|- `/app/optimize.py`\n 64| - Minimal loop that runs the public checks and benchmark.\n 65|\n 66|## What has to stay correct\n 67|\n 68|Before speed matters, the verifier checks:\n 69|\n 70|- hidden states\n 71|- convolution cache state\n 72|- recurrent SSM state\n 73|- last-token readout logits\n 74|- KL divergence between the candidate readout distribution and the reference readout distribution\n 75|- the provided public baseline must still match the fixed reference on hidden cases\n 76|\n 77|The hidden set includes:\n 78|\n 79|- long prefill\n 80|- short decode with cache reuse\n 81|- variable-length padded batches\n 82|\n 83|If correctness fails, the score is zero.\n 84|\n 85|## How to work\n 86|\n 87|Start here:\n 88|\n 89|```bash\n 90|uv run --no-sync python verify_api.py --device cuda\n 91|uv run --no-sync python run_dev_bench.py --device cuda\n 92|uv run --no-sync python optimize.py --device cuda\n 93|```\n 94|\n 95|`verify_api.py` checks that the provided port matches `transformers` on public cases.\n 96|`run_dev_bench.py` writes `/app/results/dev_benchmark.json`.\n 97|The image prepares a `.venv` at build time, so `uv run --no-sync ...` works without internet.\n 98|\n 99|## Constraints\n100|\n101|You CAN:\n102|\n103|- edit `/app/submission/candidate_impl.py` and create helper files under `/app/submission`\n104|- use `torch.compile`, Triton, custom kernels, or call into `transformers`\n105|- change internal cache layout if the returned cache still exposes compatible\n106| `conv_state`, `ssm_state`, `has_previous_state`, and decode-position semantics\n107|\n108|You CANNOT:\n109|\n110|- rely on `/tests/` or hidden verifier files\n111|- change the `CandidateBlock` constructor signature\n112|- change the `forward(hidden_states, cache=None, attention_mask=None)` signature\n113|\n114|## Time Budget\n115|\n116|Your wall-clock budget is enforced by Harbor and exposed through a timer daemon:\n117|\n118|```bash\n119|cat /app/.timer/remaining_secs\n120|cat /app/.timer/elapsed_secs\n121|test -f /app/.timer/alert_30min\n122|test -f /app/.timer/alert_10min\n123|test -f /app/.timer/alert_5min\n124|```\n125|\n126|You have a fixed wall-clock budget for this task. Plan your work to make effective use of the available time.\n127|\n128|Keep a working `/app/submission/candidate_impl.py` at all times. Leave time for a final correctness run\n129|and benchmark run.\n130|\n131|## Persisted Submission State\n132|\n133|Everything needed to run your submission at verification time must be in\n134|`/app/submission/`.\n135|\n136|Your entrypoint is `/app/submission/candidate_impl.py`. Keep any helper modules\n137|or config it needs under `/app/submission/`.\n","created_at":"2026-06-23T05:27:13.121064Z","finish_reason":null,"metrics":null},{"message_id":"d5234e7f-db34-49c6-a14f-98dda7489cab","node_id":37,"role":"tool","content":"\n 1|# Dependent Type Checker\n 2|\n 3|You are a software engineer specializing in programming language implementation.\n 4|Your goal is to implement a **correct and fast** type checker for a dependently\n 5|typed language (a subset of Martin-Löf Type Theory) in **Rust**.\n 6|\n 7|## Setup\n 8|\n 9|1. Your Rust workspace is `/app/type-checker/`. A scaffold `Cargo.toml` and\n 10| `src/main.rs` are provided as a starting point.\n 11|2. Example input files are in `/app/examples/`.\n 12|3. Check the task timer:\n 13| - `cat /app/.timer/remaining_secs`\n 14| - `cat /app/.timer/elapsed_secs`\n 15|\n 16|## Deliverable\n 17|\n 18|A Rust project at `/app/type-checker/` that compiles with `cargo build --release`\n 19|and produces a binary that type-checks input files:\n 20|\n 21|```bash\n 22|cd /app/type-checker && cargo build --release\n 23|./target/release/type-checker /app/examples/identity.sexp\n 24|```\n 25|\n 26|**Binary interface:**\n 27|- Takes one or more file paths as positional arguments\n 28|- Processes each file: parses commands, type-checks in order\n 29|- Exits with code **0** if all commands in all files type-check successfully\n 30|- Exits with code **1** if any command fails type-checking\n 31|- Prints diagnostics to **stderr** (optional, for debugging)\n 32|- Prints nothing to **stdout** (only exit codes matter)\n 33|\n 34|## Type Theory Specification\n 35|\n 36|Your checker must implement the following dependently typed language. All inputs\n 37|are **pre-elaborated** — there are no implicit arguments, no tactics, no\n 38|unification problems. Every term is fully annotated at the kernel level.\n 39|\n 40|### Core Constructs\n 41|\n 42|#### Universes (cumulative hierarchy)\n 43|\n 44|```\n 45|Type 0 : Type 1 : Type 2 : ...\n 46|```\n 47|\n 48|The universe hierarchy is cumulative: if `A : Type i` then also `A : Type j` for\n 49|any `j >= i`. Universe levels are concrete natural numbers (no universe\n 50|polymorphism variables — but universe levels in the input can be arbitrarily large).\n 51|\n 52|#### Dependent Function Types (Pi)\n 53|\n 54|```\n 55|(Pi (x : A) B) — dependent function type\n 56|(lam x e) — lambda abstraction (checked, not inferred)\n 57|(app f a) — function application\n 58|```\n 59|\n 60|**Eta-conversion for functions:** Two functions `f` and `g` of type `(Pi (x : A) B)` are\n 61|definitionally equal if `(app f x) ≡ (app g x)` for fresh `x`. Your conversion\n 62|checker **must** implement eta for functions.\n 63|\n 64|**Eta-conversion for pairs:** A pair `(pair a b)` is definitionally equal to any\n 65|term `p` of Sigma type if `a ≡ (fst p)` and `b ≡ (snd p)`. Your conversion\n 66|checker **must** handle the case where one side of a comparison is a `pair`\n 67|constructor by projecting the other side.\n 68|\n 69|#### Dependent Pair Types (Sigma)\n 70|\n 71|```\n 72|(Sigma (x : A) B) — dependent pair type\n 73|(pair a b) — pair constructor (checked against Sigma type)\n 74|(fst p) — first projection (inferred from Sigma type of p)\n 75|(snd p) — second projection (inferred from Sigma type of p)\n 76|```\n 77|\n 78|#### Let Bindings\n 79|\n 80|```\n 81|(let (x : A) v body) — let binding: x : A := v in body\n 82|```\n 83|\n 84|Let bindings are definitionally transparent: `x` unfolds to `v` during\n 85|conversion checking (delta reduction).\n 86|\n 87|#### Type Annotations\n 88|\n 89|```\n 90|(ann e A) — annotate term e with type A (switches check → infer)\n 91|```\n 92|\n 93|### General Inductive Types\n 94|\n 95|This is the most complex part of the specification. Your checker must support\n 96|**user-defined inductive types** with parameters and indices, and must\n 97|auto-generate their recursors (eliminators).\n 98|\n 99|#### Inductive Declarations\n100|\n101|An inductive type declaration has the form:\n102|\n103|```\n104|(inductive Name\n105| (params ((p1 : P1) (p2 : P2) ...))\n106| (indices ((i1 : I1) (i2 : I2) ...))\n107| (sort (Type k))\n108| (constructors\n109| ((c1 : C1_type)\n110| (c2 : C2_type)\n111| ...)))\n112|```\n113|\n114|Where:\n115|- `Name` is the type name\n116|- Parameters are fixed across all constructors (appear before the `:` in Lean notation)\n117|- Indices vary per constructor (appear after the `:`)\n118|- `sort` is the universe the type lives in\n119|- Each constructor type must be a telescope ending in an application of `Name`\n120| to the parameters and appropriate indices\n121|\n122|**Example — Natural numbers:**\n123|```\n124|(inductive Nat\n125| (params ())\n126| (indices ())\n127| (sort (Type 0))\n128| (constructors\n129| ((zero : Nat)\n130| (succ : (Pi (n : Nat) Nat)))))\n131|```\n132|\n133|**Example — Vectors (indexed by length):**\n134|```\n135|(inductive Vec\n136| (params ((A : (Type 0))))\n137| (indices ((n : Nat)))\n138| (sort (Type 0))\n139| (constructors\n140| ((vnil : (app (app Vec A) zero))\n141| (vcons : (Pi (n : Nat) (Pi (x : A) (Pi (xs : (app (app Vec A) n)) (app (app Vec A) (app succ n)))))))))\n142|```\n143|\n144|**Example — Propositional equality (indexed):**\n145|```\n146|(inductive Eq\n147| (params ((A : (Type 0)) (a : A)))\n148| (indices ((b : A)))\n149| (sort (Type 0))\n150| (constructors\n151| ((refl : (app (app (app Eq A) a) a)))))\n152|```\n153|\n154|**Example — Fin (bounded naturals):**\n155|```\n156|(inductive Fin\n157| (params ())\n158| (indices ((n : Nat)))\n159| (sort (Type 0))\n160| (constructors\n161| ((fzero : (Pi (n : Nat) (app Fin (app succ n))))\n162| (fsuc : (Pi (n : Nat) (Pi (i : (app Fin n)) (app Fin (app succ n))))))))\n163|```\n164|\n165|#### Positivity Checking\n166|\n167|All inductive definitions must pass a **strict positivity check**. A type `T`\n168|occurs strictly positively in a constructor argument type if:\n169|- `T` does not occur at all, OR\n170|- The argument type is exactly `T` applied to arguments, OR\n171|- The argument type is `(Pi (x : A) B)` where `T` does not occur in `A` and\n172| `T` occurs strictly positively in `B`\n173|\n174|`T` must **not** appear in any negative (left-hand-side of Pi) position in\n175|constructor argument types. Definitions failing positivity must be rejected.\n176|\n177|**Example of invalid definition (negative occurrence):**\n178|```\n179|(inductive Bad\n180| (params ())\n181| (indices ())\n182| (sort (Type 0))\n183| (constructors\n184| ((bad : (Pi (f : (Pi (x : Bad) Bad)) Bad)))))\n185|```\n186|This must be rejected because `Bad` appears to the left of `Pi` in `f`'s type.\n187|\n188|#### Constructor Typing\n189|\n190|After an inductive declaration, each constructor is available as a term. Given:\n191|```\n192|(inductive T (params ((p : P))) (indices ((i : I))) (sort (Type k))\n193| (constructors ((c : ))))\n194|```\n195|The constructor `c` has type `(Pi (p : P) )` — parameters are prepended.\n196|\n197|#### Recursor (Auto-Generated Eliminator)\n198|\n199|After defining an inductive type `T`, a recursor `T-rec` is automatically\n200|available. The recursor type is computed from the inductive definition:\n201|\n202|For an inductive `T` with parameters `(p1 : P1) ... (pn : Pn)`, indices\n203|`(i1 : I1) ... (im : Im)`, living in `(Type k)`, and constructors\n204|`c1 ... cj`:\n205|\n206|```\n207|T-rec : (p1 : P1) -> ... -> (pn : Pn) ->\n208| (motive : (i1 : I1) -> ... -> (im : Im) -> T p1 ... pn i1 ... im -> Type l) ->\n209| -> ... -> ->\n210| (i1 : I1) -> ... -> (im : Im) ->\n211| (target : T p1 ... pn i1 ... im) ->\n212| motive i1 ... im target\n213|```\n214|\n215|Each branch type corresponds to a constructor. For a constructor\n216|`ci : (a1 : A1) -> ... -> (ak : Ak) -> T params indices`, the branch type is:\n217|\n218|```\n219|(a1 : A1) -> ... -> (ak : Ak) ->\n220| aj)> ->\n221| motive (ci params a1 ... ak)\n222|```\n223|\n224|A \"recursive argument\" is one whose type is (or returns) `T` applied to the\n225|parameters.\n226|\n227|**Iota reduction:** Applying the recursor to a constructor head-reduces:\n228|```\n229|T-rec params motive branches... indices (ci params a1 ... ak)\n230| ~~> branch_i a1 ... ak \n231|```\n232|\n233|Where each recursive IH is computed by applying the recursor recursively:\n234|```\n235|ih_j = T-rec params motive branches... aj\n236|```\n237|\n238|### Mutual Inductive Types\n239|\n240|Your checker must support **mutually recursive** inductive type declarations\n241|using the `(mutual ...)` command:\n242|\n243|```\n244|(mutual\n245| (inductive Even (params ()) (indices ()) (sort (Type 0))\n246| (constructors\n247| ((even-zero : Even)\n248| (even-succ : (Pi (n : Odd) Even)))))\n249| (inductive Odd (params ()) (indices ()) (sort (Type 0))\n250| (constructors\n251| ((odd-succ : (Pi (n : Even) Odd))))))\n252|```\n253|\n254|All types in a mutual block are added to the context simultaneously before\n255|checking any constructors, allowing cross-references.\n256|\n257|**Positivity checking for mutual blocks:** Each type `T` in the block must\n258|occur strictly positively in ALL constructor argument types across ALL types\n259|in the block (not just its own constructors).\n260|\n261|**Mutual recursors:** The recursor for a type `T` in a mutual block takes\n262|one motive for EACH type in the block and one branch for EACH constructor\n263|across ALL types. For the Even/Odd example:\n264|\n265|```\n266|Even-rec : (P : Even -> Type l) -> (Q : Odd -> Type l) ->\n267| P even-zero ->\n268| ((n : Odd) -> Q n -> P (even-succ n)) ->\n269| ((n : Even) -> P n -> Q (odd-succ n)) ->\n270| (e : Even) -> P e\n271|```\n272|\n273|**Iota for mutual recursors:** The IH for a recursive argument of a different\n274|type uses that type's recursor with the SAME motives and branches:\n275|\n276|```\n277|Even-rec P Q base step-e step-o (even-succ n)\n278| ~~> step-e n (Odd-rec P Q base step-e step-o n)\n279|```\n280|\n281|### Universe Polymorphism\n282|\n283|Definitions and inductive types can be parameterized by **universe level\n284|variables**. This is required for writing truly generic code (e.g., a\n285|polymorphic identity function that works at any universe level).\n286|\n287|#### Universe Level Expressions\n288|\n289|```\n290|level := natural ; concrete: 0, 1, 2, ...\n291| | identifier ; level variable: u, v, l, ...\n292| | (umax level level) ; max of two levels\n293| | (usuc level) ; successor (l + 1)\n294|```\n295|\n296|#### Universe-Polymorphic Definitions\n297|\n298|```\n299|(def-poly name ((u v ...)) type body)\n300|```\n301|\n302|The level variables `u`, `v`, ... are bound in `type` and `body`. Within\n303|the definition, `(Type u)` refers to the universe at level `u`.\n304|\n305|#### Universe-Polymorphic Inductives\n306|\n307|```\n308|(inductive-poly Name ((u v ...))\n309| (params ((A : (Type u))))\n310| (indices ())\n311| (sort (Type u))\n312| (constructors ...))\n313|```\n314|\n315|#### Instantiation\n316|\n317|When using a universe-polymorphic definition or inductive, provide concrete\n318|level arguments with `(inst name (level1 level2 ...))`:\n319|\n320|```\n321|(def-poly id ((u)) (Pi (A : (Type u)) (Pi (x : A) A))\n322| (lam A (lam x x)))\n323|\n324|; Apply at universe 0\n325|(check (app (app (inst id (0)) Nat) zero) Nat)\n326|\n327|; Apply at universe 1 — works on types themselves\n328|(check (app (app (inst id (1)) (Type 0)) Nat) (Type 0))\n329|```\n330|\n331|Level expressions in `(Type ...)` must evaluate to concrete natural numbers\n332|at the point of use. The checker substitutes level variables with their\n333|concrete values and evaluates `umax`/`usuc` to produce a number.\n334|\n335|#### Universe-Polymorphic Recursors\n336|\n337|Universe-polymorphic inductives generate universe-polymorphic recursors.\n338|The recursor gains an additional level parameter for the motive's target\n339|universe:\n340|\n341|```\n342|; List is polymorphic in universe u\n343|(inductive-poly List ((u))\n344| (params ((A : (Type u))))\n345| (indices ())\n346| (sort (Type u))\n347| (constructors\n348| ((nil : (inst List (u) A))\n349| (cons : (Pi (x : A) (Pi (xs : (inst List (u) A)) (inst List (u) A)))))))\n350|\n351|; List-rec has an additional level param v for the motive universe\n352|; (inst List-rec (u v)) : (A : Type u) -> (motive : List u A -> Type v) -> ...\n353|```\n354|\n355|### Reduction and Conversion\n356|\n357|Your type checker must implement **definitional equality** via the following\n358|reductions:\n359|\n360|- **Beta reduction:** `(app (lam x e) v) ~~> e[v/x]`\n361|- **Delta reduction:** Unfold `let`-bound and top-level `def`-bound variables\n362|- **Iota reduction:** Recursor applied to constructor (see above)\n363|- **Eta for functions:** `f ≡ (lam x (app f x))` at Pi type\n364|- **Eta for pairs:** `(pair a b) ≡ p` when `a ≡ (fst p)` and `b ≡ (snd p)`\n365|\n366|The conversion checker compares terms for definitional equality. It must be:\n367|- **Correct:** Never equate terms that are not definitionally equal\n368|- **Complete (for WHNF):** Always detect equality of terms that reduce to the\n369| same weak-head normal form\n370|\n371|### Bidirectional Type Checking\n372|\n373|The checker operates in two modes:\n374|\n375|**Inference mode** (computes a type):\n376|- Variables: look up in context\n377|- `(ann e A)`: check `A` is a type, check `e : A`, return `A`\n378|- `(app f a)`: infer `f`, expect Pi type, check `a`, substitute\n379|- `(fst p)`: infer `p`, expect Sigma, return `A`\n380|- `(snd p)`: infer `p`, expect Sigma, return `B[fst p/x]`\n381|- `(let (x : A) v body)`: check `v : A`, infer `body` with `x : A := v`\n382|- `(Pi (x : A) B)`, `(Sigma (x : A) B)`: infer both, return universe\n383|- `(Type n)`: return `(Type (n+1))`\n384|- Constructors: return their declared type\n385|- Recursors: return their computed type\n386|\n387|**Checking mode** (verifies against expected type):\n388|- `(lam x e)`: expect Pi type `(Pi (x : A) B)`, check `e : B` under `x : A`\n389|- `(pair a b)`: expect Sigma type `(Sigma (x : A) B)`, check `a : A` and `b : B[a/x]`\n390|- Fall through to inference: infer type, check convertible with expected type\n391|\n392|### Universe Rules\n393|\n394|- `(Type i) : (Type (i+1))`\n395|- `(Pi (x : A) B)` where `A : Type i` and `B : Type j` lives in `Type (max i j)`\n396|- `(Sigma (x : A) B)` where `A : Type i` and `B : Type j` lives in `Type (max i j)`\n397|- Cumulativity: if `e : Type i` then `e : Type j` for `j >= i`\n398|\n399|### Large Elimination Restriction\n400|\n401|Inductives in `Type 0` (a.k.a. `Prop`-like) with more than one constructor\n402|are restricted: their recursor's motive must target `Type 0`. This prevents\n403|information-theoretic unsoundness.\n404|\n405|Specifically, an inductive in `Type 0` may eliminate into any universe only if\n406|it has **at most one constructor**. Otherwise, the recursor motive is forced\n407|to `Type 0`.\n408|\n409|## Input Format\n410|\n411|Input files use an s-expression syntax. A file is a sequence of **commands**:\n412|\n413|```\n414|; This is a comment (semicolon to end of line)\n415|\n416|; Define a new top-level term\n417|(def name type body)\n418|\n419|; Universe-polymorphic definition\n420|(def-poly name ((u v ...)) type body)\n421|\n422|; Declare an inductive type\n423|(inductive Name\n424| (params (...))\n425| (indices (...))\n426| (sort (Type k))\n427| (constructors (...)))\n428|\n429|; Universe-polymorphic inductive\n430|(inductive-poly Name ((u v ...))\n431| (params (...))\n432| (indices (...))\n433| (sort (Type level-expr))\n434| (constructors (...)))\n435|\n436|; Mutual inductive types\n437|(mutual\n438| (inductive Name1 ...)\n439| (inductive Name2 ...))\n440|\n441|; Assert that a term has a given type (standalone check)\n442|(check term type)\n443|```\n444|\n445|### Term Grammar\n446|\n447|```\n448|term := identifier ; variable or constructor/recursor\n449| | (ann term term) ; type annotation\n450| | (lam identifier term) ; lambda abstraction\n451| | (app term term) ; application\n452| | (Pi (identifier : term) term) ; dependent function type\n453| | (Sigma (identifier : term) term) ; dependent pair type\n454| | (pair term term) ; pair constructor\n455| | (fst term) ; first projection\n456| | (snd term) ; second projection\n457| | (let (identifier : term) term term) ; let binding\n458| | (Type level) ; universe\n459| | (inst identifier (level ...)) ; instantiate poly def/inductive\n460|\n461|level := natural ; concrete: 0, 1, 2\n462| | identifier ; level variable: u, v\n463| | (umax level level) ; max\n464| | (usuc level) ; successor\n465|```\n466|\n467|Identifiers: any sequence of alphanumeric characters, hyphens, underscores,\n468|and primes that does not start with a digit. Examples: `x`, `Nat`, `Vec`,\n469|`add-comm`, `x'`, `ih_1`.\n470|\n471|Natural numbers: sequences of digits (`0`, `1`, `42`, etc.).\n472|\n473|After an `(inductive T ...)` declaration:\n474|- Each constructor name `c` is available as an identifier\n475|- The recursor `T-rec` is available as an identifier\n476|\n477|Application is **binary** — multi-argument application is written as nested apps:\n478|```\n479|(app (app (app f a) b) c)\n480|```\n481|\n482|### Example Input File\n483|\n484|```\n485|; Natural numbers\n486|(inductive Nat\n487| (params ())\n488| (indices ())\n489| (sort (Type 0))\n490| (constructors\n491| ((zero : Nat)\n492| (succ : (Pi (n : Nat) Nat)))))\n493|\n494|; Addition: add n m = Nat-rec (\\_. Nat) m (\\_ ih. succ ih) n\n495|(def add (Pi (n : Nat) (Pi (m : Nat) Nat))\n496| (lam n (lam m\n497| (app (app (app (app Nat-rec\n498| (lam _ Nat))\n499| m)\n500| (lam k (lam ih (app succ ih))))\n501| n))))\n502|\n503|; Booleans\n504|(inductive Bool\n505| (params ())\n506| (indices ())\n507| (sort (Type 0))\n508| (constructors\n509| ((true : Bool)\n510| (false : Bool))))\n511|\n512|; Propositional equality\n513|(inductive Eq\n514| (params ((A : (Type 0)) (a : A)))\n515| (indices ((b : A)))\n516| (sort (Type 0))\n517| (constructors\n518| ((refl : (app (app (app Eq A) a) a)))))\n519|\n520|; Symmetry of equality\n521|; sym A a b p = Eq-rec A a (\\x _. Eq A x a) (refl A a) b p\n522|(def sym\n523| (Pi (A : (Type 0)) (Pi (a : A) (Pi (b : A) (Pi (p : (app (app (app Eq A) a) b)) (app (app (app Eq A) b) a)))))\n524| (lam A (lam a (lam b (lam p\n525| (app (app (app (app (app (app (app Eq-rec A) a)\n526| (lam x (lam _eq (app (app (app Eq A) x) a))))\n527| (app (app refl A) a))\n528| b)\n529| p))))))\n530|\n531|; 2 + 2 = 4\n532|(check\n533| (app (app refl Nat) (app (app add (app succ (app succ zero))) (app succ (app succ zero))))\n534| (app (app (app Eq Nat) (app (app add (app succ (app succ zero))) (app succ (app succ zero))))\n535| (app succ (app succ (app succ (app succ zero))))))\n536|```\n537|\n538|## What You Can Use\n539|\n540|- Pre-installed Rust toolchain (stable): `rustc`, `cargo`\n541|- Any crates from crates.io are **not** available (no internet). You must\n542| implement everything from scratch or use the Rust standard library.\n543|- The scaffold project at `/app/type-checker/` has a basic `Cargo.toml`\n544|\n545|## What You Cannot Do\n546|\n547|- Download external code or crates (no internet access)\n548|- Reference or read any scripts in `/tests/`\n549|- Wrap or shell out to any external binary for type-checking\n550|\n551|## Verification\n552|\n553|The verifier checks two things:\n554|\n555|### Correctness\n556|Your checker is tested against a collection of input files. It must correctly\n557|accept well-typed files (exit 0) and reject ill-typed files (exit non-zero).\n558|The test files cover all features described in this specification: core MLTT,\n559|general inductives, mutual inductives, universe polymorphism, eta conversion,\n560|positivity checking, and large elimination.\n561|\n562|### Throughput\n563|After correctness is verified, your checker is timed on several workloads\n564|of varying complexity. Faster is better. A naive implementation using direct\n565|substitution will be slow on normalization-heavy inputs. Optimized approaches\n566|(see below) can be significantly faster.\n567|\n568|## Performance Hints\n569|\n570|The main performance technique for dependent type checking is **Normalization\n571|by Evaluation (NbE)**:\n572|- Evaluate terms into a semantic domain (closures, not syntax)\n573|- Quote semantic values back to syntax for comparison\n574|- This avoids repeated substitution traversals\n575|\n576|Key optimization opportunities:\n577|- **Arena allocation** instead of `Rc`/`Box` for terms\n578|- **Glued evaluation**: track both evaluated and unevaluated forms\n579|- **Approximate conversion**: try fast structural comparison before full normalization\n580|- **Hash-consing** for common subterms\n581|- **Lazy unfolding**: don't unfold definitions until needed for conversion\n582|\n583|## Behavioral Rules\n584|\n585|- Never stop to ask. Work autonomously until time runs out.\n586|- Check time regularly: `cat /app/.timer/remaining_secs`\n587|- Keep your project buildable at all times.\n588|- Test against the example files frequently.\n589|- Get correctness working first — optimize only after your checker is correct.\n590|- Build incrementally: start with Pi/lam/app/Type, add Sigma, then inductives.\n591|\n592|## Time Budget\n593|\n594|You have a fixed wall-clock budget. Check the timer:\n595|\n596|```bash\n597|cat /app/.timer/remaining_secs # seconds remaining\n598|cat /app/.timer/elapsed_secs # seconds elapsed\n599|test -f /app/.timer/alert_30min # true when <=30 min remain\n600|test -f /app/.timer/alert_10min # true when <=10 min remain\n601|```\n602|\n603|You have a fixed wall-clock budget for this task. Plan your work to make effective use of the available time.\n604|\n605|Plan your work around this budget. There is a lot to implement. A checker that handles core MLTT correctly\n606|is much better than one that attempts everything but doesn't compile. Suggested\n607|priority order:\n608|1. Core type checker (Pi, lam, app, Type, let, ann, cumulative universes, Sigma)\n609|2. General inductive types (declarations, constructors, auto-generated recursors, iota)\n610|3. Eta for functions AND Sigma (pair projection), positivity checking\n","created_at":"2026-06-23T05:27:13.121072Z","finish_reason":null,"metrics":null},{"message_id":"30989267-76b1-4c30-86dc-1e36e875998f","node_id":39,"role":"assistant","content":"Let me look at a deep-swe example to compare the style.","tool_calls":[{"id":"chatcmpl-tool-86ba4eca35bded82","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/deep-swe/fastapi-implicit-head-options.md"}}],"thinking":"Now let me look at a deep-swe example to compare styles.","created_at":"2026-06-23T05:27:21.303596Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1016,"total_time_ms":8181,"input_tokens":10783,"output_tokens":62,"cache_read_tokens":21562,"cache_creation_tokens":null,"tpot_ms":115.56451612903226,"tokens_per_sec":8.653175157013258}},{"message_id":"ddd908c4-172f-4379-b955-903bb8c8babc","node_id":40,"role":"tool","content":"\n 1|GET routes lack implicit HEAD controls, and FastAPI has no OPTIONS response exposing path metadata. \n 2|\n 3|Add `auto_head` and `auto_options` to FastAPI/APIRouter constructors, decorators, `api_route`, `add_api_route`, and `include_router`. `auto_head` defaults on for GET routes; `auto_options` defaults off. \n 4|\n 5|Direct app routes use app values as outermost defaults; included-router routes resolve omitted values by nearest non-omitted setting among route, include, and router. Explicit HEAD or OPTIONS operations win. \n 6|\n 7|Implicit HEAD preserves the GET routes dependencies, status, headers, and validation behavior while returning no body. Implicit OPTIONS returns 200 JSON with `path`, ordered `methods`, and `operations`, where `operations` matches OpenAPI for that path excluding HEAD and OPTIONS, and sends `Allow`. \n 8|\n 9|Use method order `GET, HEAD, POST, PUT, PATCH, DELETE, OPTIONS, TRACE`. \n 10|\n 11|Generate one implicit OPTIONS response per path when any operation enables it. \n 12|\n 13|Public signatures exposing the new parameters must use FastAPIs `Annotated[..., Doc(...)]` style. \n 14|\n 15|Define `ImplicitMethodTrackingMiddleware` in `fastapi/middleware/methods.py`; instance methods `get_stats()` and `reset_stats()` return a deep copy shaped `{full_path: {\"head_hits\": int, \"options_hits\": int}}`, clear counts, track implicit hits only, and ignore non-HTTP scopes. \n 16|\n 17|Before editing, audit `applications.py` and `routing.py`, then trace HEAD/OPTIONS dispatch; after changes, verify precedence layers separately, repeated inclusion, method ordering, OpenAPI output, CORS preflight, docs surface, and middleware stats.\n 18|\n 19|IMPORTANT: Please work on this in a new branch from main and commit everything when you are done.\n","created_at":"2026-06-23T05:27:21.337204Z","finish_reason":null,"metrics":null},{"message_id":"cce6ab4f-ea7f-4d40-a39a-6a46b8cc386f","node_id":42,"role":"assistant","content":"I have enough research. Let me synthesize the key facts before writing the prompt:\n\n**GLM-5.2 FP4 facts (from HF model cards):**\n- `zai-org/GLM-5.2` is a 753B-param MoE (40B active), 256 routed experts (8/token), 78 layers + 1 MTP layer, DeepSeek-style MLA + DSA sparse attention, vocab 154880, hidden 6144.\n- BF16 checkpoint ~1.37–1.5 TB; NVFP4 quantized ~410–459 GB (~3× smaller).\n- NVFP4 = E2M1 weights + FP8 E4M3 block scales, 16-element blocks, produced via NVIDIA ModelOpt (`modelopt`).\n- Only MoE expert FFNs are quantized; attention (MLA + DSA indexer), router, and `lm_head` stay BF16 (SGLang's `deepseek_v2` MLA path can't consume NVFP4 attention).\n- **Requires Blackwell tensor cores** (B200 / GB200 / RTX PRO 6000 Blackwell). ~459 GB weights → needs full 8×96 GB node for weights + KV headroom.\n- vLLM v0.23.0+ supports `--quantization modelopt_fp4` (or `modelopt`); SGLang v0.5.13.post1+ also works.\n- Community NVFP4 checkpoints: `Lorbus/GLM-5.2-NVFP4`, `Mapika/GLM-5.2-NVFP4`, `lukealonso/GLM-5.2-NVFP4`.\n\n**Modal B200 facts:**\n- `@app.function(gpu=\"B200:8\")` → 8× B200 on one physical machine, 8×192 GB = 1536 GB HBM.\n- Pattern: `modal.Image.from_registry(\"nvidia/cuda:...-devel-ubuntu22.04\", add_python=\"3.12\")` + `uv_pip_install(\"vllm==...\", \"huggingface_hub[hf_transfer]\")`, mount HF/vLLM/flashinfer cache volumes, `@modal.web_server` wrapping `subprocess.Popen([\"vllm\", \"serve\", ...])`.\n- B200+ allows upgrade to B300 (needs CUDA 13.0+).\n\nHere's the prompt written in the frontier-swe / deep-swe instruction style. I'll save it to the workspace:","tool_calls":[{"id":"chatcmpl-tool-8f053ea827ec88bc","name":"write","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","content":"# GLM-5.2 NVFP4 Inference on Modal 8×B200\n\nServe `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM,\nOpenAI-compatible, with a correctness gate against the BF16 reference and a\nthroughput/latency benchmark. The verifier sends hidden prompts to your\n`/app/submission/launch_server.sh` and compares against a well-tuned\nvLLM NVFP4 baseline.\n\n## Model\n\n- **`zai-org/GLM-5.2`** — 753B-param Mixture-of-Experts (40B active), 256 routed\n experts + 1 shared (8 activated/token), 78 transformer layers + 1 MTP layer\n at index 78, DeepSeek-style MLA attention + DSA sparse-attention indexer\n (`glm_moe_dsa` architecture), hidden 6144, vocab 154880.\n- BF16 checkpoint ~1.37 TB; NVFP4 quantized ~459 GB (~3× smaller).\n- **Quantization: NVFP4** — E2M1 4-bit weights + FP8 E4M3 block scales,\n 16-element blocks, produced with NVIDIA Model Optimizer (`modelopt`).\n Only the MoE expert FFNs (routed + shared) are quantized. Attention\n (MLA projections q/kv + DSA indexer), the MoE router (`mlp.gate`), and\n `lm_head` are kept in BF16 — SGLang/vLLM's `deepseek_v2` MLA path cannot\n consume NVFP4 attention weights.\n- **KV cache: FP8** (`--kv-cache-dtype fp8`) to maximize context headroom.\n- License: MIT (pure open).\n- Use a community NVFP4 checkpoint: `Lorbus/GLM-5.2-NVFP4`,\n `Mapika/GLM-5.2-NVFP4`, or `lukealonso/GLM-5.2-NVFP4`. Pick one and pin\n the revision in `launch_server.sh`.\n\n## Hardware\n\n- **Modal 8×B200** — `@app.function(gpu=\"B200:8\")`. 8× B200 on one physical\n machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are Blackwell-only.\n- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n CUDA graphs, and runtime buffers. TP=8 is required (TP=4 needs ≥128 GB\n cards and tightens KV; TP=8 is the safe default on B200).\n- Optional: `gpu=\"B200+\"` allows Modal to upgrade to B300 (requires\n CUDA 13.0+). Only enable if your image and vLLM build are CUDA 13-compatible.\n\n## Tools / stack\n\n- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support\n and the `glm_moe_dsa` model registration. Pin a known-good version\n (e.g. `vllm==0.23.0` or newer) in the Modal image.\n- **NVIDIA Model Optimizer** (`tensorrt-model-optimizer`) — only needed if you\n re-quantize; for serving a pre-quantized HF checkpoint you do not need it.\n- **FlashInfer** — Blackwell MoE / FP4 kernels. Set\n `VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1` (and the equivalent NVFP4 env flag\n for your vLLM version) in the image env.\n- **huggingface_hub[hf_transfer]** — fast shard download (~459 GB).\n- **Modal SDK** — `modal.Image`, `modal.Volume` for HF/vLLM/flashinfer caches,\n `@modal.web_server` to expose the vLLM HTTP server.\n- **CUDA 12.8+ devel image** as the base (`nvidia/cuda:12.8.1-devel-ubuntu22.04`\n or CUDA 13.0 for B300). vLLM ships its own CUDA runtime; the devel base is\n for any JIT kernel compilation.\n\n## Files\n\n- `/app/submission/launch_server.sh`\n - **Main file you modify.** Verifier executes it with `PORT` and\n `MODEL_PATH` env vars. It must start a vLLM OpenAI-compatible server\n listening on `$PORT`.\n- `/app/submission/`\n - Owned submission root. `launch_server.sh` plus any helper scripts,\n vLLM patches, or env files must live under this tree.\n- `/app/serve_modal.py`\n - Modal entrypoint defining the image, volumes, `@app.function(gpu=\"B200:8\")`,\n and `@modal.web_server` wrapper that `subprocess.Popen`s `vllm serve`.\n- `/app/run_dev_bench.py`\n - Public dev benchmark: launches your server, sends test requests,\n reports TTFT / throughput. Hidden verifier uses different workloads.\n- `/app/verify_serving.py`\n - Sanity check that the server starts and produces coherent outputs.\n- `/app/optimize.py`\n - Convenience: verify + benchmark in sequence.\n\n## Reference serve command\n\n```bash\nvllm serve \"$MODEL_PATH\" \\\n --served-model-name glm-5.2-nvfp4 \\\n --host 0.0.0.0 --port \"$PORT\" \\\n --quantization modelopt_fp4 \\\n --kv-cache-dtype fp8 \\\n --tensor-parallel-size 8 \\\n --trust-remote-code \\\n --max-model-len 1000000 \\\n --moe-runner-backend flashinfer_cutlass \\\n --async-scheduling\n```\n\nEquivalent Modal skeleton:\n\n```python\nimport modal\n\napp = modal.App(\"glm-5.2-nvfp4\")\n\nvllm_image = (\n modal.Image.from_registry(\n \"nvidia/cuda:12.8.1-devel-ubuntu22.04\", add_python=\"3.12\"\n )\n .entrypoint([])\n .uv_pip_install(\n \"vllm==0.23.0\",\n \"huggingface_hub[hf_transfer]==0.36.0\",\n )\n .env({\n \"HF_HUB_ENABLE_HF_TRANSFER\": \"1\",\n \"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8\": \"1\",\n })\n)\n\nhf_cache = modal.Volume.from_name(\"hf-cache\", create_if_missing=True)\nvllm_cache = modal.Volume.from_name(\"vllm-cache\", create_if_missing=True)\n\nMINUTES = 60\n\n@app.function(\n image=vllm_image,\n gpu=\"B200:8\",\n scaledown_window=10 * MINUTES,\n timeout=60 * MINUTES,\n volumes={\n \"/root/.cache/huggingface\": hf_cache,\n \"/root/.cache/vllm\": vllm_cache,\n },\n)\n@modal.concurrent(max_inputs=8)\n@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\ndef serve():\n import subprocess, os\n cmd = [\n \"vllm\", \"serve\", os.environ[\"MODEL_PATH\"],\n \"--served-model-name\", \"glm-5.2-nvfp4\",\n \"--host\", \"0.0.0.0\", \"--port\", \"8000\",\n \"--quantization\", \"modelopt_fp4\",\n \"--kv-cache-dtype\", \"fp8\",\n \"--tensor-parallel-size\", \"8\",\n \"--trust-remote-code\",\n \"--max-model-len\", \"1000000\",\n \"--moe-runner-backend\", \"flashinfer_cutlass\",\n \"--async-scheduling\",\n ]\n subprocess.Popen(\" \".join(cmd), shell=True)\n```\n\n## What you can do\n\n- Tune `launch_server.sh` and `serve_modal.py`: quantization flags, KV cache\n dtype, TP/PP/EP split, `--max-model-len`, `--gpu-memory-utilization`,\n `--max-num-seq`, `--enable-chunked-prefill`, CUDA graphs (`--enforce-eager`\n vs `--no-enforce-eager`), `--async-scheduling`.\n- Patch vLLM source in site-packages (find with\n `python -c \"import vllm; print(vllm.__path__[0])\"`) — keep patches under\n `/app/submission/` and reapply at startup.\n- Swap the NVFP4 checkpoint (`Lorbus` vs `Mapika` vs `lukealonso`) and pin\n revisions; they differ in which layers are quantized and calibration data.\n- Re-quantize from `zai-org/GLM-5.2` with ModelOpt if no community checkpoint\n meets the correctness gate (slow; ~459 GB write).\n- Write custom Triton / CuTe DSL / FlashInfer kernels for the MoE FP4 GEMM\n or MLA attention and plug into vLLM.\n- Use a different serving backend (SGLang v0.5.13.post1+ with\n `--quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass`)\n if it beats vLLM on the benchmark — but the verifier invokes\n `launch_server.sh`, so the OpenAI-compatible HTTP interface must match.\n\n## What has to stay correct\n\nThe verifier runs your server and the BF16 baseline on the same hidden prompts\n(temperature=0, greedy) and compares outputs at the token level. The prompt set\nincludes normal text, long-context (>32K), degenerate inputs, and adversarial\nedge cases. NVFP4 introduces small numeric drift; outputs are expected to match\nthe BF16 reference within the gate's tolerance (typically exact greedy match\non the hidden set, but confirm the gate's tolerance in `verify_serving.py`).\nIf the correctness gate fails, your score is zero regardless of speed.\n\nWatch for:\n- DSA sparse-attention indexer must stay BF16 — quantizing it breaks correctness.\n- MLA q/kv projections must stay BF16.\n- MTP layer (index 78) handling — confirm whether your checkpoint includes it\n and whether vLLM enables MTP (`--enable-mtp` or config) consistently with the\n baseline.\n\n## Resource constraints\n\nAnalyze GPU memory before changing config (`nvidia-smi`, server logs).\nvLLM pre-allocates KV cache, CUDA graph capture, and runtime buffers — these\ncompete with the ~459 GB of weights. OOM kills the server and scores zero.\nAt TP=8 you have ~135 GB/GPU headroom after weights; pick\n`--gpu-memory-utilization` and `--max-model-len` so KV cache fits with margin\nfor CUDA graphs. First cold start downloads ~459 GB from HF — use a persistent\n`modal.Volume` for `/root/.cache/huggingface` so subsequent starts are fast.\n\n## Constraints\n\nYou CAN:\n\n- modify `/app/submission/launch_server.sh` and `serve_modal.py`, create\n helper files under `/app/submission`\n- modify vLLM source in site-packages (keep patches under `/app/submission`)\n- swap/pin the NVFP4 checkpoint and revision\n- install additional packages from the pre-built cache (no internet at runtime)\n- use `gpu=\"B200+\"` if your image is CUDA 13-compatible\n\nYou CANNOT:\n\n- access or reference `/tests/` or hidden verifier files\n- disable the timer daemon\n- access the internet at runtime (model must be cached or in the image/volume)\n- run on fewer than 8 B200s (weights do not fit)\n\n## How to work\n\n```bash\n# Quick sanity check\nuv run --no-sync python verify_serving.py\n\n# Full benchmark\nuv run --no-sync python run_dev_bench.py\n\n# Both in sequence\nuv run --no-sync python optimize.py\n```\n\nTo iterate fast, start the server manually and benchmark from another shell:\n\n```bash\n# Terminal 1\nPORT=30000 MODEL_PATH=Lorbus/GLM-5.2-NVFP4 bash /app/submission/launch_server.sh\n\n# Terminal 2\nuv run --no-sync python run_dev_bench.py --no-server --port 30000\n```\n\nFirst run: pre-warm the HF cache volume by running a one-off Modal function\nthat downloads the checkpoint once, so the served container doesn't pay the\n~459 GB download on every cold start.\n\n## Time budget\n\nWall-clock budget is enforced by Harbor and exposed via a timer daemon:\n\n```bash\ncat /app/.timer/remaining_secs\ncat /app/.timer/elapsed_secs\ntest -f /app/.timer/alert_30min\ntest -f /app/.timer/alert_10min\ntest -f /app/.timer/alert_5min\n```\n\nKeep a working `/app/submission/launch_server.sh` at all times. Leave time\nfor a final correctness check and benchmark run.\n\n## Persisted submission state\n\nEverything needed to run your submission at verification time must be in\n`/app/submission/`. Your entrypoint is `/app/submission/launch_server.sh`.\nIf your final server behavior depends on helper scripts, copied site-packages\npatches, custom kernels, or config files, keep them under `/app/submission/`\nand have `launch_server.sh` reapply or import them at startup. Direct edits in\nsite-packages alone may be lost during replay.\n"}}],"thinking":"Now I have a good sense of both styles. Let me also check the Modal docs example more carefully and the GLM-5.2 NVFP4 details. I have enough to write the prompt. Let me also verify the Modal B200:8 specifics and the vLLM version requirements.\n\nLet me check the Modal gpt_oss example for the exact pattern they use, and check the vLLM version that supports modelopt_fp4 for GLM-5.2.\n\nActually I have enough. Let me also do a quick check on whether vLLM 0.23.0+ supports GLM-5.2 modelopt_fp4 - the Lorbus HF page mentioned vLLM v0.23.0+ and `--quantization modelopt_fp4`. The Mapika page mentioned SGLang. Let me write the prompt now.\n\nThe user wants me to write a prompt in the style of these instruction.md files for the task: \"run inference with GLM 5.2 open weights FP4 on Modal 8 B200s, what tools will I need, assuming vllm and other things\".\n\nLet me synthesize. The deep-swe style is very terse/dense paragraph form. The frontier-swe style is more structured with sections (Model, Files, What you can do, Constraints, Time Budget, etc.).\n\nThe user said \"how would they write out this prompt\" - so they want me to write a prompt in that style. Let me write one that combines the structured frontier-swe style with the dense deep-swe content style, tailored to this GLM-5.2 FP4 on Modal 8xB200 task.\n\nLet me also do a quick verification on the Modal B200:8 specifics and vLLM version.\n\nActually, I have enough info. Let me write the prompt. I should ask the user where they want it saved, or just present it. Given they said \"do as much research beforehand as possible\", let me present the prompt directly and offer to save it.\n\nLet me write a comprehensive prompt in the style of these benchmarks.","created_at":"2026-06-23T05:28:43.255521Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1426,"total_time_ms":81916,"input_tokens":543,"output_tokens":3927,"cache_read_tokens":32344,"cache_creation_tokens":null,"tpot_ms":20.496562261268142,"tokens_per_sec":48.78866939992545}},{"message_id":"ae41e2b6-c5e6-40e0-b1d5-6aa4962d61a6","node_id":43,"role":"tool","content":"File created successfully at: /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","created_at":"2026-06-23T05:28:43.271130Z","finish_reason":null,"metrics":null},{"message_id":"cdbc13ce-85bb-4345-84f7-71d8f9698787","node_id":45,"role":"assistant","content":"Saved to .\n\n## Summary of what you'll need\n\n**Tools / stack:**\n- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support and the `glm_moe_dsa` model registration.\n- **NVIDIA Model Optimizer (`tensorrt-model-optimizer`)** — only if you re-quantize yourself; not needed to serve a pre-quantized HF checkpoint.\n- **FlashInfer** — Blackwell MoE / FP4 kernels (`VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1`, `--moe-runner-backend flashinfer_cutlass`).\n- **huggingface_hub[hf_transfer]** — fast ~459 GB shard download.\n- **Modal SDK** — `gpu=\"B200:8\"`, `modal.Volume` for HF/vLLM caches, `@modal.web_server` wrapping `vllm serve`.\n- **CUDA 12.8+ devel base image** (`nvidia/cuda:12.8.1-devel-ubuntu22.04`); CUDA 13.0 if you opt into `B200+`/B300.\n- A **community NVFP4 checkpoint**: `Lorbus/GLM-5.2-NVFP4`, `Mapika/GLM-5.2-NVFP4`, or `lukealonso/GLM-5.2-NVFP4`.\n\n**Key gotchas the prompt encodes:**\n- NVFP4 quantizes **only the MoE expert FFNs**; MLA attention, the DSA sparse-attention indexer, the router, and `lm_head` must stay BF16 (SGLang/vLLM's `deepseek_v2` MLA path can't consume NVFP4 attention).\n- **TP=8 is mandatory** — ~459 GB weights need the full 8×192 GB node; TP=4 only fits on ≥128 GB cards and tightens KV.\n- KV cache in **FP8** (`--kv-cache-dtype fp8`) for context headroom.\n- **B200 is Blackwell-only territory** for NVFP4 tensor cores; Hopper can't run it.\n- Pre-warm a persistent HF cache volume so cold starts don't re-download 459 GB.\n\n**Style notes on how deep-swe / frontier-swe write these:**\n- frontier-swe uses structured sections: *Model → Files → What you can do → What has to stay correct → Resource constraints → Constraints → How to work → Time budget → Persisted submission state*, with a concrete reference command and a \"verifier scores you against a tuned baseline\" framing.\n- deep-swe is denser — single terse paragraph, no headers, imperative spec with explicit precedence/edge-case rules and a final \"audit then trace then verify\" line.\n- I went with the frontier-swe structured form since this is a serving/infra task (matches `inference-system-optimization.md`), but folded in deep-swe-style density in the correctness and quantization-scope notes.","tool_calls":[],"created_at":"2026-06-23T05:28:55.609641Z","finish_reason":"stop","metrics":{"ttft_ms":1715,"total_time_ms":12337,"input_tokens":3966,"output_tokens":683,"cache_read_tokens":32886,"cache_creation_tokens":null,"tpot_ms":15.551976573938507,"tokens_per_sec":64.30050837883637}},{"message_id":"3b64aa0b-3354-49e6-96d0-a8b61d401c31","node_id":46,"role":"user","content":"should we use nvidia dynamo, should we disagregate prefill and deccopde?","created_at":"2026-06-23T05:29:14.088134Z","finish_reason":null,"metrics":null},{"message_id":"9d535c03-3fe7-41a4-9547-9bb593d0828a","node_id":48,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8e54c8828114587c","name":"web_search","arguments":{"query":"NVIDIA Dynamo inference framework disaggregated prefill decode"}},{"id":"chatcmpl-tool-a020f2fa3256220c","name":"web_search","arguments":{"query":"vLLM disaggregated prefill decode throughput latency tradeoff"}},{"id":"chatcmpl-tool-b53da009ca8072de","name":"web_search","arguments":{"query":"NVIDIA Dynamo vLLM SGLang integration Blackwell"}}],"thinking":"Good question. Let me research NVIDIA Dynamo and whether disaggregated prefill/decode makes sense for GLM-5.2 NVFP4 on 8×B200.","created_at":"2026-06-23T05:29:17.496507Z","finish_reason":"tool_calls","metrics":{"ttft_ms":2550,"total_time_ms":3405,"input_tokens":21838,"output_tokens":98,"cache_read_tokens":15090,"cache_creation_tokens":null,"tpot_ms":8.724489795918368,"tokens_per_sec":114.61988304093568}},{"message_id":"2194c7b7-c06c-462d-b4af-bf41bcb7dc34","node_id":49,"role":"tool","content":"# Web Search Results for \"NVIDIA Dynamo inference framework disaggregated prefill decode\"\n\n## 1. Disaggregated Serving | NVIDIA Dynamo Documentation\nURL: https://docs.dynamo.nvidia.com/dynamo/design-docs/disaggregated-serving\n\n# Disaggregated Serving\n...\nThe prefill and decode phases of LLM requests have different computation characteristics and memory footprints. Disaggregating these phases into specialized llm engines allows for better hardware allocation, improved scalability, and overall enhanced performance. For example, using a larger TP for the memory-bound decoding phase while a smaller TP for the computation-bound prefill phase allows both phases to be computed efficiently. In addition, for requests with long context, separating their prefill phase into dedicated prefill engines allows the ongoing decoding requests to be efficiently processed without being blocked by these long prefills.\n...\nDisaggregated execution of a request has three main steps:\n...\n1. Prefill engine computes prefill phase and generates KV cache\n2. Prefill engine transfers the KV cache to decode engine\n3. Decode engine computes decode phase.\n...\nThe key to high-performance disaggregation is efficient KV transfer. Dynamo leverages NIXL to transfer KV cache directly from the VRAM of the prefill engine to the VRAM of the decode engine. The KV transfer is non-blocking, allowing GPU forward passes to continue serving other requests during the transfer.\n...\nThe disaggregated serving flow is orchestrated by the `PrefillRouter`:\n...\n1. Worker Selection: The router selects a prefill worker using KV-aware routing (based on cache overlap scores and load) or simple load balancing.\n2. Prefill Execution: The router sends the prefill request to the selected prefill worker. The prefill worker computes the KV cache and returns `disaggregated_params` containing backend-specific transfer metadata.\n3. Decode Routing: The router injects the prefill result into the decode request, then routes to the decode worker.\n4. KV Transfer: The decode worker uses the transfer metadata to coordinate with the prefill worker. NIXL handles the direct GPU-to-GPU transfer using the optimal available transport (NVLink, InfiniBand/UCX, etc.).\n...\n- ...\n\n## 2. Disaggregated Serving | NVIDIA Dynamo Documentation\nURL: https://docs.nvidia.com/dynamo/dev/components/router/disaggregated-serving\n\n# Disaggregated Serving\n...\nDynamo supports disaggregated serving where prefill (prompt processing) and decode (token generation) are handled by separate worker pools. When you register workers with `ModelType.Prefill`, the frontend automatically detects them and activates an internal prefill router.\n...\n## Automatic Prefill Router Activation\n...\nThe prefill router is automatically created when:\n...\n1. A decode model is registered, for example via `register_model()` with `ModelType.Chat | ModelType.Completions`.\n2. A prefill worker is detected with the same model name and `ModelType.Prefill`.\n...\n- Always disables active block tracking (`track_active_blocks=false`) since prefill workers do not perform decode.\n- Seamlessly integrates into the request pipeline between preprocessing and decode routing.\n- Falls back gracefully to decode-only mode if prefill fails or no prefill workers are available.\n...\nKey characteristics of the decode routing stage in disaggregated mode:\n...\n- Disables overlap scoring (`overlap_score_credit=0`) because decode routing should not chase prefix reuse.\n- Disables KV reuse assumption (`assume_kv_reuse=false`) unless the backend can truly deduplicate transferred blocks.\n- Disables prefill-token tracking (`track_prefill_tokens=false`) so decode-side load reflects decode work rather than already-completed prompt work.\n...\nWhen both workers are registered, requests are automatically routed.\n...\n```python\n#\n...\nworker registration (in\n...\n_endpoint =\n...\nawait register_model(\n model_input=ModelInput.Tokens,\n model_type=ModelType.Chat | ModelType.Completions,\n endpoint=decode_endpoint,\n model_name=\"meta-llama/Llama-2-7b-hf\",\n # ... other parameters\n)\n...\nawait decode_endpoint.serve_endpoint(decode_handler.generate)\n...\n# Prefill worker registration (in your prefill worker)\nprefill_endpoint = runtime.endpoint(\"dynamo.prefill.generate\")\n\nawait register_model(\n model_input=ModelInput.Tokens,\n model_type=ModelType.Prefill,\n en...\n\n## 3. Architecture Flow | NVIDIA Dynamo Documentation\nURL: https://docs.dynamo.nvidia.com/dynamo/design-docs/architecture-flow\n\nThis diagram shows the NVIDIA Dynamo disaggregated inference system. Color-coded flows indicate different types of operations.\n...\n1. Request (S1): HTTP client sends API request to Frontend (OpenAI-compatible server on port 8000)\n2. Preprocess (S2): Frontend preprocesses the request (applies chat template, tokenizes) and validates it\n3. Route to Prefill (S3): PrefillRouter selects a prefill worker using KV-aware routing or load balancing\n...\n1. Prefill (S4): Prefill worker executes the prefill computation on the input tokens and generates KV cache\n2. Return Metadata (S5): Prefill worker returns `disaggregated_params` containing backend-specific transfer metadata\n...\n1. Route to Decode (S6): PrefillRouter injects prefill result into decode request and routes to decode worker\n2. KV Transfer (S7): Decode worker coordinates with prefill worker for direct GPU-to-GPU KV cache transfer via NIXL\n...\n1. Decode (S8): Decode worker generates tokens using the transferred KV cache\n...\n2. Response (S9): Generated tokens stream back through Frontend for post-processing (detokenization) and delivery to Client\n...\n- The `PrefillRouter` sits between the Frontend and workers, orchestrating disaggregated serving\n- Selects prefill workers using KV-aware routing (cache overlap scores + load) or simple load balancing\n- Injects transfer metadata into decode requests for KV cache coordination\n...\n-to-\n...\n- Transfer metadata exchanged via `disaggregated_params` in prefill response\n- Backend-specific coordination: SGLang uses bootstrap connections, TRTLLM uses opaque state, vLLM uses block IDs\n...\n- Each worker maintains local KV cache in its GPU memory\n...\n- No shared storage bottlenecks—transfers are direct worker-to-worker via NIXL\n...\n- Non-blocking transfers allow GPU forward passes\n...\ncontinue during KV transfer\n...\n%% Worker Layer\n subgraph WL[\"Worker Layer\"]\n %% Prefill Worker\n PrefillWorker[\"Prefill Worker
Computes KV Cache\"]\n S4[[\"<...\n\n## 4. Dynamo Disaggregation: Separating Prefill and Decode for Enhanced Performance | NVIDIA Dynamo Documentation\nURL: https://docs.nvidia.com/dynamo/v-0-8-1/design-docs/disaggregated-serving\n\nThe prefill and decode phases of LLM requests have different computation characteristics and memory footprints. Disaggregating these phases into specialized llm engines allows for better hardware allocation, improved scalability, and overall enhanced performance. For example, using a larger TP for the memory-bound decoding phase while a smaller TP for the computation-bound prefill phase allows both phases to be computed efficiently. In addition, for requests with long context, separating their prefill phase into dedicated prefill engines allows the ongoing decoding requests to be efficiently processed without being blocked by these long prefills.\n...\nHowever, not all requests’ prefill phases need to be computed in the remote prefill engine. If the prefill is short or the decode engine has a high prefix cache hit, often it is more efficient to prefill locally in the decode engine. The disaggregation design in Dynamo accounts for all these scenarios and features a flexible framework that delivers strong performance across various conditions.\n...\nWhen worker receives a request, it first decides if the prefill should be done locally or remotely using the disaggregated router and allocates the KV blocks. If prefilling remotely, it then pushes a remote prefill request to the prefill queue. After that, the prefill worker pulls from prefill queue, reads KV blocks with prefix cache hit from the worker, computes the prefill, and writes the computed KV blocks back to the worker. Finally, the worker completes the remaining decoding.\n...\nNot all requests’ prefill phases need to be computed in the remote prefill engine. Disaggregated router decides whether the prefill phase of a request should be computed locally and globally at runtime based on the prefill length and prefill queue status. Specifically, a request is sent to remote prefill engine if the following two conditions are met:\n...\nPrefill requests are computation bound (except for very short prefills) and should be execu...\n\n## 5. Scale and Serve Generative AI | NVIDIA Dynamo\nURL: https://www.nvidia.com/en-us/ai/dynamo/\n\nNVIDIA Dynamo is an open source, distributed inference-serving framework built to deploy models in multi-node environments at data center scale. It supports open source inference engines—including SGLang, NVIDIA TensorRT™ LLM, and vLLM—and simplifies the complexities of distributed serving by disaggregating inference phases across different GPUs, intelligently routing requests to the appropriate GPU to avoid redundant computation, and extending GPU memory through data caching to cost-effective storage tiers.\n...\nThe GB200 NVL72 connects 72 GPUs via high-speed NVIDIA NVLink™, enabling low-latency expert communication critical for MoE reasoning models. NVIDIA Dynamo enhances efficiency through disaggregated inference, splitting prefill and decode phases across nodes for independent optimization. Together, GB200 NVL72 and NVIDIA Dynamo form a high-performance stack optimized for large-scale MoE inference.\n...\n### Disaggregated Serving\n...\nSeparates large language model (LLM) context and generation phases across distinct GPUs, enabling independent GPU allocation and optimization to increase requests served per GPU.\n...\nRemoves the guesswork from disaggregated servi… (137 chars truncated)\n… (8 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/37ae7d2f/content.txt\n","created_at":"2026-06-23T05:29:18.830586Z","finish_reason":null,"metrics":null},{"message_id":"bce5a4d6-b056-4e40-9a53-02a56692d2aa","node_id":50,"role":"tool","content":"# Web Search Results for \"vLLM disaggregated prefill decode throughput latency tradeoff\"\n\n## 1. Disaggregated Prefilling (experimental) - vLLM\nURL: https://docs.vllm.ai/en/stable/features/disagg_prefill/\n\nThis page introduces you to the disaggregated prefilling feature in vLLM.\n...\n- Tuning time-to-first-token (TTFT) and inter-token-latency (ITL) separately. Disaggregated prefilling put prefill and decode phase of LLM inference inside different vLLM instances. This gives you the flexibility to assign different parallel strategies (e.g. `tp` and `pp`) to tune TTFT without affecting ITL, or to tune ITL without affecting TTFT.\n...\n- Controlling tail ITL. Without disaggregated prefilling, vLLM may insert some prefill jobs during the decoding of one request. This results in higher tail latency. Disaggregated prefilling helps you solve this issue and control tail ITL. Chunked prefill with a proper chunk size also can achieve the same goal, but in practice it's hard to figure out the correct chunk size value. So disaggregated prefilling is a much more reliable way to control tail ITL.\n...\nDisaggregated prefill DOES NOT improve throughput.\n...\nWe implement disaggregated prefilling by running 2 vLLM instances. One for prefill (we call it prefill instance) and one for decode (we call it decode instance), and then use a connector to transfer the prefill KV caches and results from prefill instance to decode instance.\n\n## 2. Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation | vLLM Blog\nURL: https://vllm.ai/blog/2026-04-07-moriio-kv-connector\n\nTL;DR: Prefill and decode fight over the same GPUs, causing ITL spikes under load. We show how to disaggregate them on a single 8-GPU MI300X node using AMD's MORI-IO connector — achieving 2.5x higher goodput compared to standard collocated serving on the same 8 GPUs, with stable token generation. Benchmark uses Qwen3-235B-A22B-FP8 at 8 req/s with 2000-token prompts and 1000-token outputs — see Table 3 and Experimental Details for full configuration.\n...\nWhen both phases share the same instance, they interfere with each other. Prefill requests can block dozens of ongoing decode streams, leading to visible stuttering, while decode workloads delay the scheduling of new prefills. The result is a system where neither phase runs efficiently nor predictably.\n...\nStandard serving fails because ITL concentrates in two clusters — the high-latency cluster at ~150ms far exceeds the 50ms threshold. Both disaggregated modes eliminate ITL violations entirely; their remaining failures are TTFT exceedances as request rate climbs. Write mode edges out read mode (73 vs 70) because concurrent proxy dispatch lowers TTFT, keeping more requests below the 1s threshold.\n...\nIn a standard deployment, prefill and decode share the same vLLM engine and compete for scheduling within each batch. A single prefill — processing all input tokens in one forward pass — takes significantly longer than a decode step. Every decode request in the same batch waits for that prefill to finish before generating its next token, directly inflating ITL.\n...\nWith disaggregation, your decode engine runs exclusively decode batches. No compute-intensive prefill jobs interrupt the step cadence, so ITL becomes stable and predictable regardless of how many new requests are entering the system. This benefit is identical in both read mode and write mode — the decode engine is isolated from prefill in either case.\n...\nWrite mode eliminates Overhead 1. Because the proxy dispatches to both instances concurrently, the decode ...\n\n## 3. Serving Agentic Workloads at Scale with vLLM x Mooncake | vLLM Blog\nURL: https://vllm.ai/blog/2026-05-06-mooncake-store\n\nTL;DR: Agentic workloads generate massive shared prefixes that are often recomputed across turns. By integrating Mooncake's distributed KV cache store into vLLM, we achieve 3.8x higher throughput, 46x lower TTFT, and 8.6x lower end-to-end latency on realistic agentic traces, while scaling nearly linearly to 60 GB200 GPUs.\n...\nTakeaway: we can\n...\nservice as a set of isolated vLL\n...\nthat provides both\n...\naggregate capacity and cross-\n...\ncache hits.\n...\nMooncake is an open-source, high-performance library for KV cache transfer and distributed storage. vLLM has already adopted Mooncake for prefill-decode (PD) disaggregation via the MooncakeConnector, using Mooncake's transfer engine to move KV caches between GPUs. Now, we take that integration one step further by building a distributed KV cache pool with Mooncake Store.\n...\nPrefill: The prefill instance prepares KV blocks for the PD connector and also stores them in the distributed KV cache pool through the store connector. For cache hits, vLLM queries all connectors and can recover matching prefixes from the Mooncake Store connector.\n...\nDecode: When the decode instance writes KV blocks into the distributed pool, they immediately become visible to prefill instances. Decode itself does not currently read from the pool: because vLLM schedules each request to both a prefill and a decode instance, the prefill instance loads any prefix KV blocks from the pool and forwards them to decode through the PD connector.\n...\nWe ran the Kimi-2.5 NVFP4 model on GB200 nodes with PD disaggregation. The prefill instance used TP4, while the decode instance used DP8 + EP. We found that this configuration provided the best latency-throughput tradeoff.\n...\nFigure 4: vLLM with Mooncake Store vs. baseline on realistic Codex agentic traces (1P1D, 12 GB200 GPUs). The distributed KV cache pool improves throughput by 3.8x, reduces P50 TTFT by 46x, and reduces E2E latency by 8.6x, driven by a cache hit rate increase from 1.7% to 92.2%.\n...\nThe ...\n\n## 4. [Docs]: comprehensive rewrite of disaggregated prefilling (PD) documentation · Pull Request #38525 · vllm-project/vllm\nURL: https://github.com/vllm-project/vllm/pull/38525\n\nThe existing disaggregated prefilling documentation is mostly example-oriented and lacks a clear explanation of system design,\ndeployment requirements, and production usage. Users may find it difficult to:\n...\n- Understand when to use PD disaggregation\n- Deploy Prefill and Decode instances correctly\n- Choose appropriate KV cache connectors\n- Reason about latency trade-offs (TTFT vs ITL)\n...\nThis PR introduces a comprehensive rewrite of the `disagg_prefill.md` documentation, covering:\n...\n- Clear explanation of Prefill–Decode (PD) disaggregation\n- When to use it (TTFT, ITL, tail latency control)\n- Explicit clarification that it optimizes latency, not throughput\n...\n- End-to-end pipeline:\n\nRequest → Prefill → KV Transfer → Decode\n...\n- Request routing\n - KV cache generation and transfer\n - Decode execution\n...\n- Detailed introduction of all supported connectors\n- Connector comparison table\n- Design considerations:\n...\n- latency vs throughput\n - network capabilities\n - memory constraints\n - system complexity\n...\n> **Review (commented):**\n> \n> ## Code Review\n> \n> This pull request significantly expands and reorganizes the documentation for Prefill-Decode Disaggregation in vLLM. It introduces detailed sections on the architecture, deployment requirements, and a comprehensive comparison of available connector implementations. A critical feedback point highlights a contradiction in the documentation regarding throughput improvements, which should be resolved to avoid user confusion.\n\n## 5. [Misc] benchmark supports disaggregated prefill\nURL: https://github.com/vllm-project/vllm/pull/16824\n\nWhen conducting a benchmark, calculating throughput as (input + output) divided by the total time is generally correct. However, during disaggregated prefill, since prefill and decode do not affect each other, concurrency can be set very high.\n...\nBut if the num_req is fixed at a value like 100 and the concurrency is set to 50, theoretically, when the prefill node finishes processing the last 50 requests, it will be idling while the decode node is still in the decode phase. Moreover, as the output length increases, the bubble (idle time) will become even larger.\n...\nTherefore, in the case of disaggregated prefill, the total throughput should be calculated as:\n​​throughput = (num_input_token / pd_prefill_duration + num_output_token / total_duration)​\n","created_at":"2026-06-23T05:29:18.830589Z","finish_reason":null,"metrics":null},{"message_id":"5b8b612f-9d7a-460a-aa98-eb4af3b7ca5b","node_id":51,"role":"tool","content":"# Web Search Results for \"NVIDIA Dynamo vLLM SGLang integration Blackwell\"\n\n## 1. Reference Guide | NVIDIA Dynamo Documentation\nURL: https://docs.nvidia.com/dynamo/backends/sg-lang/reference-guide\n\nThe SGLang backend in Dynamo uses a modular architecture where `main.py` dispatches to specialized initialization modules based on the worker type. Each worker type has its own init module, request handler, health check, and registration logic.\n...\nDynamo SGLang uses SGLang's native argument parser -- all SGLang engine arguments (e.g., `--model-path`, `--tp`, `--trust-remote-code`) are passed through directly. Dynamo adds its own arguments for worker mode selection, tokenizer control, and disaggregation configuration.\n...\n| Argument | Env Var | Default | Description |\n| --- | --- | --- | --- |\n| `--endpoint` | `DYN_ENDPOINT` | Auto-generated | Dynamo endpoint in `dyn://namespace.component.endpoint` format |\n| `--use-sglang-tokenizer` | `DYN_SGL_USE_TOKENIZER` | `false` | [Deprecated] Use `--dyn-chat-processor sglang` on the frontend instead. See SGLang Chat Processor. |\n| `--dyn-tool-call-parser` | `DYN_TOOL_CALL_PARSER` | `None` | Tool call parser (overrides SGLang's `--tool-call-parser`) |\n| `--dyn-reasoning-parser` | `DYN_REASONING_PARSER` | `None` | Reasoning parser for chain-of-thought models |\n| `--custom-jinja-template` | `DYN_CUSTOM_JINJA_TEMPLATE` | `None` | Custom chat template path (incompatible with `--use-sglang-tokenizer`) |\n...\n| `--embedding-worker` | `DYN_SGL_EMBEDDING_WORKER` | `false` | Run as embedding worker (also sets SGLang's `--is-embedding`) |\n...\n| `--multimodal-encode-worker` | `DYN_SGL_MULTIMODAL_ENCODE_WORKER` | `false` | Run as multimodal encode worker (frontend-facing) |\n...\n| `--multimodal-worker` | `DYN_SGL_MULTIMODAL_WORKER` | `false` | Run as multimodal LLM worker |\n...\n| `--image-diffusion-worker` | `DYN_SGL_IMAGE_DIFFUSION_WORKER` | `false` | Run as image diffusion worker |\n| `--video-generation-worker` | `DYN_SGL_VIDEO_GENERATION_WORKER` | `false` | Run as video generation worker |\n...\n| `--disagg-config` | `DYN_SGL_DISAGG_CONFIG` | `None` | Path to YAML disaggregation config file |\n| `--disagg-config-key` | `DYN_SGL_DISAGG_CONF...\n\n## 2. Examples | NVIDIA Dynamo Documentation\nURL: https://docs.nvidia.com/dynamo/latest/backends/sg-lang/examples\n\nFor quick start instructions, see the SGLang README. This document provides all deployment patterns for running SGLang with Dynamo, including LLMs, multimodal, and diffusion models, and Kubernetes deployment.\n...\n- etcd is optional but is\n...\n. You can also use `--discovery-backend file` to use file system based discovery.\n- NATS is only needed when using KV routing with events (`--kv-events-config`). Use `--no-router-kv-events` on the frontend for prediction-based routing without NATS.\n- On Kubernetes, neither is required when using the Dynamo operator (`DYN_DISCOVERY_BACKEND=kubernetes`).\n...\nEach launch script runs the frontend and worker(s) in a single terminal. You can run each command separately in different terminals for testing. For AI agents working with Dynamo, you can run the launch script in the background and use the `curl` commands to test the deployment.\n...\n## LLM Serving\n...\n### Aggregated Serving\n...\nThe simplest deployment pattern: a single worker handles both prefill and decode.\n...\n```bash\ncd $DYNAMO_HOME/examples/backends/sglang\n./launch/agg.sh\n...\n### Aggregated Serving with KV Routing\n...\nTwo workers behind a KV-aware router that maximizes cache reuse:\n...\n```bash\ncd $DYNAMO_HOME/examples/backends/sglang\n./launch/agg_router.sh\n...\nThis launches the frontend with `--router-mode kv` and two workers with ZMQ-based KV event publishing.\n...\n### Disaggregated Serving\n...\nSeparates prefill and decode into independent workers connected via NIXL for KV cache transfer. Requires 2 GPUs.\n...\n```bash\ncd $DYNAMO_HOME/examples/backends/sglang\n./launch/disagg.sh\n...\nFor details on how SGLang disaggregation works with Dynamo, including the bootstrap mechanism and RDMA transfer flow, see SGLang Disaggregation.\n...\n### Disaggregated Serving with KV-Aware Prefill Routing\n...\nScales to 2 prefill + 2 decode workers with KV-aware routing on both pools. Requires 4 GPUs.\n...\n```bash\ncd $DYNAMO_HOME/examples/backends/sglang\n./launch/disagg_router.sh\n\n```\n...\nThe front...\n\n## 3. SGLang\nURL: https://docs.nvidia.com/dynamo/backends/sg-lang.md\n\nDynamo SGLang integrates SGLang engines into Dynamo's distributed runtime, enabling disaggregated serving, KV-aware routing, and request cancellation while maintaining full compatibility with SGLang's native engine arguments. It supports LLM inference, embedding models, multimodal vision models, and diffusion-based generation (LLM, image, video).\n...\nsglang\n...\nfrom NGC. See\n...\nartifacts for the current\n...\nand CUDA variants.\n...\n#### Build from source inside upstream SGLang container\n...\nand launch the upstream\n...\nang image, then\n...\nrun --gpus all -it --rm \\\n --network host --shm-size=10G \\\n --ulimit memlock=-1 --ulimit stack=67108864 \\\n --ulimit nofile=65536:65536 \\\n --ipc host \\\n lmsysorg/sglang:v{sglang_version}\n...\n## Feature Support Matrix\n...\n| Feature | Status | Notes |\n| --- | --- | --- |\n| Disaggregated Serving | ✅ | Prefill/decode separation with NIXL KV transfer |\n| KV-Aware Routing | ✅ | |\n| SLA-Based Planner | ✅ | |\n| Multimodal Support | ✅ | Image via EPD, E/PD, E/P/D patterns |\n| Diffusion Models | ✅ | LLM diffusion, image, and video generation |\n| Request Cancellation | ✅ | Aggregated full; disaggregated decode-only |\n| Graceful Shutdown | ✅ | Discovery unregister + grace period |\n| Observability | ✅ | Metrics, tracing, and Grafana dashboards |\n| KVBM | ❌ | Planned |\n...\n### Multi-Node TP\n...\nSGLang supports multi-node tensor parallelism via the native `--dist-init-addr`, `--nnodes`, and `--node-rank` flags. See SGLang server arguments for the canonical reference; the same flags work with `python -m dynamo.sglang`. For a Kubernetes deployment example, see `disagg-multinode.yaml`.\n...\n### Kubernetes Deployment\n...\nYou can deploy SGLang with Dynamo on Kubernetes using a `DynamoGraphDeployment`. For more details, see the SGLang Kubernetes Deployment Guide.\n\n## 4. SGLang Multimodal | NVIDIA Dynamo Documentation\nURL: https://docs.dynamo.nvidia.com/dynamo/user-guides/multimodality-support/sg-lang-multimodal\n\nThis document provides a comprehensive guide for multimodal inference using SGLang backend in Dynamo. SGLang multimodal supports EPD, E/PD, and E/P/D flows, with NIXL (RDMA) for zero-copy tensor transfer in disaggregated modes.\n...\n` | Vision encoder separate |\n...\n| ✅ | `multimodal_\n...\nKV cache via bootstrap |\n...\n| Component | Flag | Purpose |\n| --- | --- | --- |\n| Processor | `--multimodal-processor` | HTTP entry, OpenAI→SGLang conversion |\n| Encode Worker | `--multimodal-encode-worker` | Vision encoder, embeddings generation |\n| PD Worker | `--multimodal-worker` | Prefill + Decode with embeddings |\n| Decode Worker | `--multimodal-worker --serving-mode=decode` | Entry point for disaggregation |\n| Prefill Worker | `--multimodal-worker --serving-mode=prefill` | Called by Decode, bootstrap coordination |\n...\n- NIXL\n...\n: Embeddings transferred from Encoder → PD Worker using NIXL\n...\nExpansion: Single `<|image_pad|>` token replaced with N tokens based on embedding shape\n...\nmodel (AutoModel)\n...\nThe `MultimodalEncodeWorker` downloads and encodes the image and passes the embeddings to the MultimodalWorker. The work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface. The `MultimodalWorker` then prefills and decodes the prompt in the same engine, as in the LLM aggregated serving example. Only the processor is registered to the Dynamo frontend as an available endpoint. Workers do NOT register - they are internal components and communicate via NATS.\n...\nIn models like Qwen2.5-VL, embeddings are only required during the prefill stage. The image embeddings are transferred via NIXL from the Encode Worker to the Decode Worker (the entry point for disaggregation), which then coordinates with the Prefill Worker. The Prefill Worker processes the embeddings and forwards the KV cache back to the Decode Worker for token generation.\n...\nvLLM\n...\n- SGLang: Frontend → Processor → Encode → Decode → Prefill (Decode is entry p...\n\n## 5. SGLang\nURL: https://docs.dynamo.nvidia.com/dynamo/backends/sg-lang.md\n\nDynamo SGLang integrates SGLang engines into Dynamo's distributed runtime, enabling disaggregated serving, KV-aware routing, and request cancellation while maintaining full compatibility with SGLang's native engine arguments. It supports LLM inference, embedding models, multimodal vision models, and diffusion-based generation (LLM, image, video).\n...\nsglang\n...\nfrom NGC. See\n...\nartifacts for the current\n...\nand CUDA variants.\n...\n#### Build from source inside upstream SGLang container\n...\nand launch the upstream\n...\nang image, then\n...\nrun --gpus all -it --rm \\\n --network host --shm-size=10G \\\n --ulimit memlock=-1 --ulimit stack=67108864 \\\n --ulimit nofile=65536:65536 \\\n --ipc host \\\n lmsysorg/sglang:v{sglang_version}\n...\n## Feature Support Matrix\n...\n| Feature | Status | Notes |\n| --- | --- | --- |\n| Disaggregated Serving | ✅ | Prefill/decode separation with NIXL KV transfer |\n| KV-Aware Routing | ✅ | |\n| SLA-Based Planner | ✅ | |\n| Multimodal Support | ✅ | Image via EPD, E/PD, E/P/D patterns |\n| Diffusion Models | ✅ | LLM diffusion, image, and video generation |\n| Request Cancellation | ✅ | Aggregated full; disaggregated decode-only |\n| Graceful Shutdown | ✅ | Discovery unregister + grace period |\n| Observability | ✅ | Metrics, tracing, and Grafana dashboards |\n| KVBM | ❌ | Planned |\n...\n### Multi-Node TP\n...\nSGLang supports multi-node tensor parallelism via the native `--dist-init-addr`, `--nnodes`, and `--node-rank` flags. See SGLang server arguments for the … (138 chars truncated)\n… (4 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/9b715215/content.txt\n","created_at":"2026-06-23T05:29:18.830592Z","finish_reason":null,"metrics":null},{"message_id":"38d72737-9b28-4f20-9de5-ac0b3b04647e","node_id":53,"role":"assistant","content":"Short answer: **Dynamo — probably not on a single Modal node. Disaggregation — not on 8 B200s, but yes if you can use 16+ (2 nodes).** Here's the reasoning:\n\n## NVIDIA Dynamo\n\n**What it is:** A distributed serving framework that wraps vLLM / SGLang / TRT-LLM as backends and adds disaggregated prefill/decode (via NIXL GPU-to-GPU KV transfer), KV-aware routing, an SLA planner, and a unified OpenAI frontend. Designed for **multi-node, data-center scale** (GB200 NVL72, multi-node TP).\n\n**On a single 8×B200 Modal node — skip it.** Dynamo's value props (cross-node disaggregation, KV-aware routing across many workers, SLA planner) don't help when you have one model replica on one node. You'd pay the overhead (etcd/NATS discovery, NIXL, frontend router) with no benefit. Plain `vllm serve` or `sglang serve` is simpler, faster to iterate on, and has fewer moving parts for the verifier to break on.\n\n**When to reach for Dynamo:** If you scale to **2+ Modal 8×B200 nodes** (16+ GPUs). Then Dynamo gives you disaggregation, KV-aware prefix routing across nodes, and a clean OpenAI frontend for free — exactly the scenario it's built for. The vLLM×Mooncake blog ran Kimi-2.5 NVFP4 on GB200 nodes with PD disaggregation and got 3.8× throughput / 46× lower TTFT on agentic traces with shared prefixes.\n\n## Disaggregated prefill/decode\n\n**The hard constraint: GLM-5.2-NVFP4 weights are ~459 GB.** On 8×B200 (192 GB each = 1536 GB total):\n\n| Config | Weights/GPU | KV headroom/GPU | Fits? |\n|--------|------------|-----------------|-------|\n| TP=8 (all 8 GPUs, one engine) | ~57 GB | ~135 GB | Yes, comfortable |\n| TP=4 prefill + TP=4 decode (split the node) | ~115 GB | ~77 GB | Tight — short context only |\n| TP=8 prefill + TP=8 decode (2 nodes) | ~57 GB | ~135 GB each | Yes, this is the real disaggregated setup |\n\n**On a single 8×B200 node: no.** You can't run two full TP=8 engines on 8 GPUs. The only within-node option is TP=4+TP=4, which leaves only ~77 GB/GPU for KV cache — too tight for GLM-5.2's 1M-token context window, and the KV transfer overhead between two TP=4 groups eats the latency win. Use **chunked prefill** (`--enable-chunked-prefill` with a tuned chunk size) instead — it gives you most of the ITL-stability benefit of disaggregation without splitting the node.\n\n**On 2+ nodes (16+ B200s): yes, strongly consider it.** The vLLM docs are explicit: *\"Disaggregated prefill DOES NOT improve throughput\"* — it's a **latency / tail-ITL** optimization. So it depends on what the benchmark measures:\n\n- If the verifier scores **geomean speedup across sequential + concurrent workloads** (as in the frontier-swe `inference-system-optimization` prompt): disaggregation helps the concurrent case (stable ITL, no prefill blocking decode) but is neutral-to-negative for sequential single-request latency (KV transfer adds a hop). Net: marginal.\n- If workloads have **long prefills + short decodes** (agentic, RAG, long-context summarization): disaggregation wins big — long prefills stop blocking decode streams.\n- If workloads have **shared prefixes** (multi-turn agentic): pair disaggregation with a distributed KV store (Mooncake Store) — the vLLM blog showed 92% cache hit rate vs 1.7%, 3.8× throughput.\n\n**Recommended split for GLM-5.2-NVFP4 on 2× 8×B200:**\n- Prefill node: TP=8 (computation-bound, wants max compute)\n- Decode node: DP=2 × TP=4 + EP (memory-bound, wants max batch size / KV capacity; expert parallelism fits MoE well)\n- KV transfer: NIXL (Dynamo) or MooncakeConnector (vLLM native) over NVLink/IB\n\n## Recommendation for the prompt\n\nI'd update the prompt to:\n1. **Default path (single 8×B200 node):** plain vLLM, TP=8, chunked prefill, no Dynamo, no disaggregation. This is what fits and what the verifier expects.\n2. **Stretch path (2+ nodes):** add a section on Dynamo + disaggregation as an opt-in if the user can provision 16 B200s, with the TP=8-prefill / DP2×TP=4-EP-decode split and Mooncake/NIXL KV transfer.\n\nWant me to update the prompt file with these two paths, or keep it single-node only?","tool_calls":[],"thinking":"Now I have enough info to give a grounded recommendation. Let me think through this carefully.\n\nKey facts:\n1. **NVIDIA Dynamo** is a distributed inference-serving framework that wraps vLLM/SGLang/TRT-LLM as backends. It provides:\n - Disaggregated prefill/decode via NIXL (GPU-to-GPU KV transfer)\n - KV-aware routing\n - SLA-based planner\n - Multi-node TP support\n - Designed for data-center scale, multi-node, GB200 NVL72\n\n2. **Disaggregated prefill/decode** — the key insight from vLLM docs: \"Disaggregated prefill DOES NOT improve throughput.\" It improves:\n - TTFT (separately tunable)\n - Tail ITL (decode not blocked by long prefills)\n - Allows different TP/PP for prefill vs decode\n\n3. **Modal 8×B200 context**:\n - Single node, 8 GPUs, 192GB each = 1536 GB total\n - GLM-5.2 NVFP4 = ~459 GB weights, needs TP=8 just to fit\n - This is the critical constraint: **you cannot split 8 B200s into prefill pool + decode pool** because the model weights alone need all 8 GPUs at TP=8.\n\nSo the answer is nuanced:\n\n**Should you use NVIDIA Dynamo?**\n- Dynamo is designed for multi-node / data-center scale. On a single 8-GPU Modal node, its overhead (etcd/NATS discovery, NIXL, frontend router) adds complexity without much benefit.\n- Dynamo's main value props (disaggregation across node pools, KV-aware routing across many workers, SLA planner) shine when you have multiple nodes / many GPUs.\n- On a single node with one model replica, plain vLLM (or SGLang) is simpler and faster to iterate on.\n- HOWEVER: if you're scaling to multiple Modal 8×B200 nodes (e.g. 2+ nodes), Dynamo becomes very attractive — it gives you disaggregation, KV-aware routing, and a clean OpenAI frontend for free.\n\n**Should you disaggregate prefill and decode?**\n- On a SINGLE 8×B200 node: **No, you can't meaningfully disaggregate.** The model needs all 8 GPUs at TP=8 just to hold weights. You can't run a separate prefill engine and decode engine — each would need its own 8-GPU replica (16 GPUs total). That's 2 nodes minimum.\n- The vLLM blog shows disaggregation on a single 8-GPU MI300X node with Qwen3-235B — but that's because Qwen3-235B-FP8 fits in fewer GPUs, leaving room to split. GLM-5.2-NVFP4 at 459 GB does NOT leave room — it needs all 8.\n- **If you have 2+ Modal 8×B200 nodes (16+ GPUs):** Yes, disaggregation is worth it. The Mooncake/vLLM blog on Kimi-2.5 NVFP4 on GB200 used TP4 prefill + DP8+EP decode. For GLM-5.2 you'd do something like:\n - Prefill node: TP=8 (or TP=4 if it fits... it doesn't at 459GB/4=115GB > 96GB... wait B200 is 192GB, so 459/4 = 115GB < 192GB. TP=4 COULD work on B200!)\n - Actually wait — B200 has 192GB. 459/4 = 115GB per GPU. That fits! So you could do TP=4 prefill on 4 GPUs and TP=4 decode on the other 4 GPUs of the same node. But that's tight on KV cache.\n - Or: 1 node TP=8 prefill, 1 node TP=8 decode (or DP2×TP=4 decode with EP).\n\nLet me reconsider the single-node disaggregation question:\n\nB200 = 192GB. GLM-5.2-NVFP4 = 459GB.\n- TP=8: 57GB/GPU weights, 135GB/GPU for KV. ✓ comfortable\n- TP=4: 115GB/GPU weights, 77GB/GPU for KV. Tight but might work for shorter contexts.\n- TP=2: 230GB/GPU — doesn't fit.\n\nSo on a single 8×B200 node, you COULD theoretically do:\n- 4 GPUs for prefill (TP=4)\n- 4 GPUs for decode (TP=4)\n\nBut this is very tight. KV cache at TP=4 with 77GB/GPU headroom is limited, especially for long-context (GLM-5.2 supports 1M context). And the KV transfer between the two TP=4 groups within the same node would go over NVLink (fast), but you'd need NIXL or a vLLM connector.\n\nHowever, the more natural split for a 753B MoE on 8×B200 is:\n- Use all 8 for both phases (aggregated) with chunked prefill to control ITL\n- OR use 2 nodes: 1 for prefill (TP=8), 1 for decode (TP=8 or DP2×TP=4+EP)\n\nThe vLLM docs explicitly say: \"Disaggregated prefill DOES NOT improve throughput.\" It's a latency/tail-ITL optimization. If the benchmark is throughput-oriented (geomean speedup), disaggregation won't help and may hurt (KV transfer overhead, idle prefill bubbles). If the benchmark cares about TTFT and tail ITL under concurrent load, disaggregation helps a lot.\n\nLooking at the frontier-swe prompt I wrote: \"The verifier tests both sequential single-request latency and concurrent batched requests at multiple concurrency levels.\" So it's latency-focused under concurrency. Disaggregation could help with tail ITL under concurrent load.\n\nBut the constraint is: can you fit two engines on 8 B200s? TP=4+TP=4 is very tight. More realistic: you need 2 nodes (16 B200s).\n\nLet me give a clear, structured recommendation.","created_at":"2026-06-23T05:30:09.334138Z","finish_reason":"stop","metrics":{"ttft_ms":1619,"total_time_ms":50502,"input_tokens":6696,"output_tokens":2453,"cache_read_tokens":36927,"cache_creation_tokens":null,"tpot_ms":19.92784345699144,"tokens_per_sec":50.18104453490989}},{"message_id":"ac6f7962-dd0a-4475-b427-d967bb85c03c","node_id":54,"role":"user","content":"okay 8 b200s, get working, single node, ","created_at":"2026-06-23T05:30:43.394990Z","finish_reason":null,"metrics":null},{"message_id":"eac59b5f-6d77-4f35-aab9-4f6d5bdc5d96","node_id":55,"role":"assistant","content":"","tool_calls":[],"thinking":"The user wants me to actually","created_at":"2026-06-23T05:30:45.276212Z","finish_reason":null,"metrics":{"ttft_ms":1864,"total_time_ms":1878,"input_tokens":null,"output_tokens":0,"cache_read_tokens":null,"cache_creation_tokens":null,"tpot_ms":0,"tokens_per_sec":0}},{"message_id":"34498843-10e5-4adc-94d7-734accbc3cfb","node_id":56,"role":"user","content":"wheres the prompt","created_at":"2026-06-23T05:30:53.077282Z","finish_reason":null,"metrics":null},{"message_id":"2ee666df-c21d-4ca9-b4d9-eeea5cc180ab","node_id":58,"role":"assistant","content":"The prompt is at . Let me tighten it to single-node 8×B200 only, drop the Dynamo/disaggregation ambiguity, and fold in chunked-prefill as the ITL-stability lever.","tool_calls":[{"id":"chatcmpl-tool-8f6b095300f4211c","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md"}}],"thinking":"The user wants me to update the prompt for single-node 8×B200 only, no Dynamo, no disaggregation. Let me update the existing prompt file to be cleaner and more focused on the single-node path, removing the multi-node/disaggregation ambiguity.\n\nLet me re-read the current prompt and revise it.","created_at":"2026-06-23T05:30:57.714438Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1290,"total_time_ms":4636,"input_tokens":7,"output_tokens":181,"cache_read_tokens":44723,"cache_creation_tokens":null,"tpot_ms":18.486187845303867,"tokens_per_sec":54.09444112372982}},{"message_id":"8e81f1fa-3d1b-4514-99ba-474d27f542a5","node_id":59,"role":"tool","content":"\n 1|# GLM-5.2 NVFP4 Inference on Modal 8×B200\n 2|\n 3|Serve `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM,\n 4|OpenAI-compatible, with a correctness gate against the BF16 reference and a\n 5|throughput/latency benchmark. The verifier sends hidden prompts to your\n 6|`/app/submission/launch_server.sh` and compares against a well-tuned\n 7|vLLM NVFP4 baseline.\n 8|\n 9|## Model\n 10|\n 11|- **`zai-org/GLM-5.2`** — 753B-param Mixture-of-Experts (40B active), 256 routed\n 12| experts + 1 shared (8 activated/token), 78 transformer layers + 1 MTP layer\n 13| at index 78, DeepSeek-style MLA attention + DSA sparse-attention indexer\n 14| (`glm_moe_dsa` architecture), hidden 6144, vocab 154880.\n 15|- BF16 checkpoint ~1.37 TB; NVFP4 quantized ~459 GB (~3× smaller).\n 16|- **Quantization: NVFP4** — E2M1 4-bit weights + FP8 E4M3 block scales,\n 17| 16-element blocks, produced with NVIDIA Model Optimizer (`modelopt`).\n 18| Only the MoE expert FFNs (routed + shared) are quantized. Attention\n 19| (MLA projections q/kv + DSA indexer), the MoE router (`mlp.gate`), and\n 20| `lm_head` are kept in BF16 — SGLang/vLLM's `deepseek_v2` MLA path cannot\n 21| consume NVFP4 attention weights.\n 22|- **KV cache: FP8** (`--kv-cache-dtype fp8`) to maximize context headroom.\n 23|- License: MIT (pure open).\n 24|- Use a community NVFP4 checkpoint: `Lorbus/GLM-5.2-NVFP4`,\n 25| `Mapika/GLM-5.2-NVFP4`, or `lukealonso/GLM-5.2-NVFP4`. Pick one and pin\n 26| the revision in `launch_server.sh`.\n 27|\n 28|## Hardware\n 29|\n 30|- **Modal 8×B200** — `@app.function(gpu=\"B200:8\")`. 8× B200 on one physical\n 31| machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are Blackwell-only.\n 32|- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n 33| CUDA graphs, and runtime buffers. TP=8 is required (TP=4 needs ≥128 GB\n 34| cards and tightens KV; TP=8 is the safe default on B200).\n 35|- Optional: `gpu=\"B200+\"` allows Modal to upgrade to B300 (requires\n 36| CUDA 13.0+). Only enable if your image and vLLM build are CUDA 13-compatible.\n 37|\n 38|## Tools / stack\n 39|\n 40|- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support\n 41| and the `glm_moe_dsa` model registration. Pin a known-good version\n 42| (e.g. `vllm==0.23.0` or newer) in the Modal image.\n 43|- **NVIDIA Model Optimizer** (`tensorrt-model-optimizer`) — only needed if you\n 44| re-quantize; for serving a pre-quantized HF checkpoint you do not need it.\n 45|- **FlashInfer** — Blackwell MoE / FP4 kernels. Set\n 46| `VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1` (and the equivalent NVFP4 env flag\n 47| for your vLLM version) in the image env.\n 48|- **huggingface_hub[hf_transfer]** — fast shard download (~459 GB).\n 49|- **Modal SDK** — `modal.Image`, `modal.Volume` for HF/vLLM/flashinfer caches,\n 50| `@modal.web_server` to expose the vLLM HTTP server.\n 51|- **CUDA 12.8+ devel image** as the base (`nvidia/cuda:12.8.1-devel-ubuntu22.04`\n 52| or CUDA 13.0 for B300). vLLM ships its own CUDA runtime; the devel base is\n 53| for any JIT kernel compilation.\n 54|\n 55|## Files\n 56|\n 57|- `/app/submission/launch_server.sh`\n 58| - **Main file you modify.** Verifier executes it with `PORT` and\n 59| `MODEL_PATH` env vars. It must start a vLLM OpenAI-compatible server\n 60| listening on `$PORT`.\n 61|- `/app/submission/`\n 62| - Owned submission root. `launch_server.sh` plus any helper scripts,\n 63| vLLM patches, or env files must live under this tree.\n 64|- `/app/serve_modal.py`\n 65| - Modal entrypoint defining the image, volumes, `@app.function(gpu=\"B200:8\")`,\n 66| and `@modal.web_server` wrapper that `subprocess.Popen`s `vllm serve`.\n 67|- `/app/run_dev_bench.py`\n 68| - Public dev benchmark: launches your server, sends test requests,\n 69| reports TTFT / throughput. Hidden verifier uses different workloads.\n 70|- `/app/verify_serving.py`\n 71| - Sanity check that the server starts and produces coherent outputs.\n 72|- `/app/optimize.py`\n 73| - Convenience: verify + benchmark in sequence.\n 74|\n 75|## Reference serve command\n 76|\n 77|```bash\n 78|vllm serve \"$MODEL_PATH\" \\\n 79| --served-model-name glm-5.2-nvfp4 \\\n 80| --host 0.0.0.0 --port \"$PORT\" \\\n 81| --quantization modelopt_fp4 \\\n 82| --kv-cache-dtype fp8 \\\n 83| --tensor-parallel-size 8 \\\n 84| --trust-remote-code \\\n 85| --max-model-len 1000000 \\\n 86| --moe-runner-backend flashinfer_cutlass \\\n 87| --async-scheduling\n 88|```\n 89|\n 90|Equivalent Modal skeleton:\n 91|\n 92|```python\n 93|import modal\n 94|\n 95|app = modal.App(\"glm-5.2-nvfp4\")\n 96|\n 97|vllm_image = (\n 98| modal.Image.from_registry(\n 99| \"nvidia/cuda:12.8.1-devel-ubuntu22.04\", add_python=\"3.12\"\n100| )\n101| .entrypoint([])\n102| .uv_pip_install(\n103| \"vllm==0.23.0\",\n104| \"huggingface_hub[hf_transfer]==0.36.0\",\n105| )\n106| .env({\n107| \"HF_HUB_ENABLE_HF_TRANSFER\": \"1\",\n108| \"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8\": \"1\",\n109| })\n110|)\n111|\n112|hf_cache = modal.Volume.from_name(\"hf-cache\", create_if_missing=True)\n113|vllm_cache = modal.Volume.from_name(\"vllm-cache\", create_if_missing=True)\n114|\n115|MINUTES = 60\n116|\n117|@app.function(\n118| image=vllm_image,\n119| gpu=\"B200:8\",\n120| scaledown_window=10 * MINUTES,\n121| timeout=60 * MINUTES,\n122| volumes={\n123| \"/root/.cache/huggingface\": hf_cache,\n124| \"/root/.cache/vllm\": vllm_cache,\n125| },\n126|)\n127|@modal.concurrent(max_inputs=8)\n128|@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\n129|def serve():\n130| import subprocess, os\n131| cmd = [\n132| \"vllm\", \"serve\", os.environ[\"MODEL_PATH\"],\n133| \"--served-model-name\", \"glm-5.2-nvfp4\",\n134| \"--host\", \"0.0.0.0\", \"--port\", \"8000\",\n135| \"--quantization\", \"modelopt_fp4\",\n136| \"--kv-cache-dtype\", \"fp8\",\n137| \"--tensor-parallel-size\", \"8\",\n138| \"--trust-remote-code\",\n139| \"--max-model-len\", \"1000000\",\n140| \"--moe-runner-backend\", \"flashinfer_cutlass\",\n141| \"--async-scheduling\",\n142| ]\n143| subprocess.Popen(\" \".join(cmd), shell=True)\n144|```\n145|\n146|## What you can do\n147|\n148|- Tune `launch_server.sh` and `serve_modal.py`: quantization flags, KV cache\n149| dtype, TP/PP/EP split, `--max-model-len`, `--gpu-memory-utilization`,\n150| `--max-num-seq`, `--enable-chunked-prefill`, CUDA graphs (`--enforce-eager`\n151| vs `--no-enforce-eager`), `--async-scheduling`.\n152|- Patch vLLM source in site-packages (find with\n153| `python -c \"import vllm; print(vllm.__path__[0])\"`) — keep patches under\n154| `/app/submission/` and reapply at startup.\n155|- Swap the NVFP4 checkpoint (`Lorbus` vs `Mapika` vs `lukealonso`) and pin\n156| revisions; they differ in which layers are quantized and calibration data.\n157|- Re-quantize from `zai-org/GLM-5.2` with ModelOpt if no community checkpoint\n158| meets the correctness gate (slow; ~459 GB write).\n159|- Write custom Triton / CuTe DSL / FlashInfer kernels for the MoE FP4 GEMM\n160| or MLA attention and plug into vLLM.\n161|- Use a different serving backend (SGLang v0.5.13.post1+ with\n162| `--quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass`)\n163| if it beats vLLM on the benchmark — but the verifier invokes\n164| `launch_server.sh`, so the OpenAI-compatible HTTP interface must match.\n165|\n166|## What has to stay correct\n167|\n168|The verifier runs your server and the BF16 baseline on the same hidden prompts\n169|(temperature=0, greedy) and compares outputs at the token level. The prompt set\n170|includes normal text, long-context (>32K), degenerate inputs, and adversarial\n171|edge cases. NVFP4 introduces small numeric drift; outputs are expected to match\n172|the BF16 reference within the gate's tolerance (typically exact greedy match\n173|on the hidden set, but confirm the gate's tolerance in `verify_serving.py`).\n174|If the correctness gate fails, your score is zero regardless of speed.\n175|\n176|Watch for:\n177|- DSA sparse-attention indexer must stay BF16 — quantizing it breaks correctness.\n178|- MLA q/kv projections must stay BF16.\n179|- MTP layer (index 78) handling — confirm whether your checkpoint includes it\n180| and whether vLLM enables MTP (`--enable-mtp` or config) consistently with the\n181| baseline.\n182|\n183|## Resource constraints\n184|\n185|Analyze GPU memory before changing config (`nvidia-smi`, server logs).\n186|vLLM pre-allocates KV cache, CUDA graph capture, and runtime buffers — these\n187|compete with the ~459 GB of weights. OOM kills the server and scores zero.\n188|At TP=8 you have ~135 GB/GPU headroom after weights; pick\n189|`--gpu-memory-utilization` and `--max-model-len` so KV cache fits with margin\n190|for CUDA graphs. First cold start downloads ~459 GB from HF — use a persistent\n191|`modal.Volume` for `/root/.cache/huggingface` so subsequent starts are fast.\n192|\n193|## Constraints\n194|\n195|You CAN:\n196|\n197|- modify `/app/submission/launch_server.sh` and `serve_modal.py`, create\n198| helper files under `/app/submission`\n199|- modify vLLM source in site-packages (keep patches under `/app/submission`)\n200|- swap/pin the NVFP4 checkpoint and revision\n201|- install additional packages from the pre-built cache (no internet at runtime)\n202|- use `gpu=\"B200+\"` if your image is CUDA 13-compatible\n203|\n204|You CANNOT:\n205|\n206|- access or reference `/tests/` or hidden verifier files\n207|- disable the timer daemon\n208|- access the internet at runtime (model must be cached or in the image/volume)\n209|- run on fewer than 8 B200s (weights do not fit)\n210|\n211|## How to work\n212|\n213|```bash\n214|# Quick sanity check\n215|uv run --no-sync python verify_serving.py\n216|\n217|# Full benchmark\n218|uv run --no-sync python run_dev_bench.py\n219|\n220|# Both in sequence\n221|uv run --no-sync python optimize.py\n222|```\n223|\n224|To iterate fast, start the server manually and benchmark from another shell:\n225|\n226|```bash\n227|# Terminal 1\n228|PORT=30000 MODEL_PATH=Lorbus/GLM-5.2-NVFP4 bash /app/submission/launch_server.sh\n229|\n230|# Terminal 2\n231|uv run --no-sync python run_dev_bench.py --no-server --port 30000\n232|```\n233|\n234|First run: pre-warm the HF cache volume by running a one-off Modal function\n235|that downloads the checkpoint once, so the served container doesn't pay the\n236|~459 GB download on every cold start.\n237|\n238|## Time budget\n239|\n240|Wall-clock budget is enforced by Harbor and exposed via a timer daemon:\n241|\n242|```bash\n243|cat /app/.timer/remaining_secs\n244|cat /app/.timer/elapsed_secs\n245|test -f /app/.timer/alert_30min\n246|test -f /app/.timer/alert_10min\n247|test -f /app/.timer/alert_5min\n248|```\n249|\n250|Keep a working `/app/submission/launch_server.sh` at all times. Leave time\n251|for a final correctness check and benchmark run.\n252|\n253|## Persisted submission state\n254|\n255|Everything needed to run your submission at verification time must be in\n256|`/app/submission/`. Your entrypoint is `/app/submission/launch_server.sh`.\n257|If your final server behavior depends on helper scripts, copied site-packages\n258|patches, custom kernels, or config files, keep them under `/app/submission/`\n259|and have `launch_server.sh` reapply or import them at startup. Direct edits in\n260|site-packages alone may be lost during replay.\n","created_at":"2026-06-23T05:30:57.742075Z","finish_reason":null,"metrics":null},{"message_id":"391ed726-b13a-4307-b1e8-3538ffcb2249","node_id":60,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1/Downloads/utilities/benchmarks-prompts (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Monday, 2026-06-22\n\n","created_at":"2026-06-23T05:31:04.839096Z","finish_reason":null,"metrics":null},{"message_id":"f35c55c6-5b38-4ec2-9737-40fe689fa282","node_id":61,"role":"user","content":"continue","created_at":"2026-06-23T05:31:08.178417Z","finish_reason":null,"metrics":null},{"message_id":"9460c69f-7ded-4618-a88b-6aa87e887560","node_id":63,"role":"assistant","content":"Tightening the prompt to single-node only — removing the Dynamo/disaggregation mentions, the `B200+`/B300 opt-in, and the multi-node stretch path, and adding chunked-prefill as the primary ITL lever.","tool_calls":[{"id":"chatcmpl-tool-ae94937f16c2394f","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":"- **Modal 8×B200** — `@app.function(gpu=\"B200:8\")`. 8× B200 on one physical\n machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are Blackwell-only.\n- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n CUDA graphs, and runtime buffers. TP=8 is required (TP=4 needs ≥128 GB\n cards and tightens KV; TP=8 is the safe default on B200).\n- Optional: `gpu=\"B200+\"` allows Modal to upgrade to B300 (requires\n CUDA 13.0+). Only enable if your image and vLLM build are CUDA 13-compatible.","new_string":"- **Modal 8×B200, single node** — `@app.function(gpu=\"B200:8\")`. 8× B200 on\n one physical machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are\n Blackwell-only. This is the entire GPU budget — no second node, no\n disaggregated prefill/decode pools. One vLLM engine, TP=8, collocated\n prefill+decode.\n- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n CUDA graphs, and runtime buffers. TP=8 is mandatory: TP=4 would split the\n node into two 4-GPU engines and leave only ~77 GB/GPU for KV — too tight for\n GLM-5.2's context window, and you cannot run two TP=8 engines on 8 GPUs.\n- **No disaggregation.** Disaggregated prefill/decode requires a second\n 8×B200 node (one TP=8 prefill + one TP=8 decode with NIXL/Mooncake KV\n transfer). On a single node, use **chunked prefill**\n (`--enable-chunked-prefill` with a tuned `--max-num-batched-tokens` chunk\n size) to get the ITL-stability benefit of disaggregation without splitting\n the GPU pool."}}],"thinking":"The user wants me to continue — tighten the prompt to single-node 8×B200 only, drop the Dynamo/disaggregation ambiguity, fold in chunked-prefill as the ITL-stability lever. Let me rewrite the file.","created_at":"2026-06-23T05:31:24.072362Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1411,"total_time_ms":15891,"input_tokens":3784,"output_tokens":641,"cache_read_tokens":44730,"cache_creation_tokens":null,"tpot_ms":22.589703588143525,"tokens_per_sec":44.267955801104975}},{"message_id":"f0863d2e-a22c-45f5-a538-59838d27e2c2","node_id":64,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 26| the revision in `launch_server.sh`.\n 27|\n 28|## Hardware\n 29|\n 30|- **Modal 8×B200, single node** — `@app.function(gpu=\"B200:8\")`. 8× B200 on\n 31| one physical machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are\n 32| Blackwell-only. This is the entire GPU budget — no second node, no\n 33| disaggregated prefill/decode pools. One vLLM engine, TP=8, collocated\n 34| prefill+decode.\n 35|- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n 36| CUDA graphs, and runtime buffers. TP=8 is mandatory: TP=4 would split the\n 37| node into two 4-GPU engines and leave only ~77 GB/GPU for KV — too tight for\n 38| GLM-5.2's context window, and you cannot run two TP=8 engines on 8 GPUs.\n 39|- **No disaggregation.** Disaggregated prefill/decode requires a second\n 40| 8×B200 node (one TP=8 prefill + one TP=8 decode with NIXL/Mooncake KV\n 41| transfer). On a single node, use **chunked prefill**\n 42| (`--enable-chunked-prefill` with a tuned `--max-num-batched-tokens` chunk\n 43| size) to get the ITL-stability benefit of disaggregation without splitting\n 44| the GPU pool.\n 45|\n 46|## Tools / stack\n 47|\n 48|- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support","created_at":"2026-06-23T05:31:24.107199Z","finish_reason":null,"metrics":null},{"message_id":"77a602b4-1fd7-4af6-bf78-0c72f50030fb","node_id":66,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-97238fb393d6cbe3","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":"- **CUDA 12.8+ devel image** as the base (`nvidia/cuda:12.8.1-devel-ubuntu22.04`\n or CUDA 13.0 for B300). vLLM ships its own CUDA runtime; the devel base is\n for any JIT kernel compilation.","new_string":"- **CUDA 12.8+ devel image** as the base\n (`nvidia/cuda:12.8.1-devel-ubuntu22.04`). vLLM ships its own CUDA runtime;\n the devel base is for any JIT kernel compilation."}}],"created_at":"2026-06-23T05:31:30.064341Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":5956,"input_tokens":1125,"output_tokens":176,"cache_read_tokens":48513,"cache_creation_tokens":null,"tpot_ms":33.84090909090909,"tokens_per_sec":29.550033579583612}},{"message_id":"30b17b8f-7789-4de4-9ccb-772a7f5735aa","node_id":67,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 55| for your vLLM version) in the image env.\n 56|- **huggingface_hub[hf_transfer]** — fast shard download (~459 GB).\n 57|- **Modal SDK** — `modal.Image`, `modal.Volume` for HF/vLLM/flashinfer caches,\n 58| `@modal.web_server` to expose the vLLM HTTP server.\n 59|- **CUDA 12.8+ devel image** as the base\n 60| (`nvidia/cuda:12.8.1-devel-ubuntu22.04`). vLLM ships its own CUDA runtime;\n 61| the devel base is for any JIT kernel compilation.\n 62|\n 63|## Files\n 64|\n 65|- `/app/submission/launch_server.sh`","created_at":"2026-06-23T05:31:30.101059Z","finish_reason":null,"metrics":null},{"message_id":"4cd93165-c6d4-4336-9ab4-25e231b4feda","node_id":69,"role":"assistant","content":"Now update the reference serve command and Modal skeleton to add chunked prefill, and remove the `B200+` opt-in from the Constraints section.","tool_calls":[{"id":"chatcmpl-tool-88a5515912ffc40d","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":"```bash\nvllm serve \"$MODEL_PATH\" \\\n --served-model-name glm-5.2-nvfp4 \\\n --host 0.0.0.0 --port \"$PORT\" \\\n --quantization modelopt_fp4 \\\n --kv-cache-dtype fp8 \\\n --tensor-parallel-size 8 \\\n --trust-remote-code \\\n --max-model-len 1000000 \\\n --moe-runner-backend flashinfer_cutlass \\\n --async-scheduling\n```","new_string":"```bash\nvllm serve \"$MODEL_PATH\" \\\n --served-model-name glm-5.2-nvfp4 \\\n --host 0.0.0.0 --port \"$PORT\" \\\n --quantization modelopt_fp4 \\\n --kv-cache-dtype fp8 \\\n --tensor-parallel-size 8 \\\n --trust-remote-code \\\n --max-model-len 1000000 \\\n --moe-runner-backend flashinfer_cutlass \\\n --enable-chunked-prefill \\\n --max-num-batched-tokens 8192 \\\n --gpu-memory-utilization 0.92 \\\n --async-scheduling\n```\n\n`--max-num-batched-tokens` is the chunked-prefill chunk size. Tune it: smaller\n(e.g. 2048–4096) favors stable ITL under concurrent decode; larger (e.g.\n16384–32768) favors prefill throughput / TTFT. The hidden benchmark tests both\nserial and concurrent workloads — pick a value that balances both, or make it\nconfigurable and sweep it during dev."}}],"created_at":"2026-06-23T05:31:40.988457Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1120,"total_time_ms":10886,"input_tokens":403,"output_tokens":432,"cache_read_tokens":49637,"cache_creation_tokens":null,"tpot_ms":22.60648148148148,"tokens_per_sec":44.23510137210731}},{"message_id":"44b56aca-ec78-4210-866f-98824533ea5a","node_id":70,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 81| - Convenience: verify + benchmark in sequence.\n 82|\n 83|## Reference serve command\n 84|\n 85|```bash\n 86|vllm serve \"$MODEL_PATH\" \\\n 87| --served-model-name glm-5.2-nvfp4 \\\n 88| --host 0.0.0.0 --port \"$PORT\" \\\n 89| --quantization modelopt_fp4 \\\n 90| --kv-cache-dtype fp8 \\\n 91| --tensor-parallel-size 8 \\\n 92| --trust-remote-code \\\n 93| --max-model-len 1000000 \\\n 94| --moe-runner-backend flashinfer_cutlass \\\n 95| --enable-chunked-prefill \\\n 96| --max-num-batched-tokens 8192 \\\n 97| --gpu-memory-utilization 0.92 \\\n 98| --async-scheduling\n 99|```\n100|\n101|`--max-num-batched-tokens` is the chunked-prefill chunk size. Tune it: smaller\n102|(e.g. 2048–4096) favors stable ITL under concurrent decode; larger (e.g.\n103|16384–32768) favors prefill throughput / TTFT. The hidden benchmark tests both\n104|serial and concurrent workloads — pick a value that balances both, or make it\n105|configurable and sweep it during dev.\n106|\n107|Equivalent Modal skeleton:\n108|\n109|```python","created_at":"2026-06-23T05:31:41.023420Z","finish_reason":null,"metrics":null},{"message_id":"2eb15535-588d-4e44-9cc3-8a7be1b76d49","node_id":72,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b3877b4892a20418","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":" cmd = [\n \"vllm\", \"serve\", os.environ[\"MODEL_PATH\"],\n \"--served-model-name\", \"glm-5.2-nvfp4\",\n \"--host\", \"0.0.0.0\", \"--port\", \"8000\",\n \"--quantization\", \"modelopt_fp4\",\n \"--kv-cache-dtype\", \"fp8\",\n \"--tensor-parallel-size\", \"8\",\n \"--trust-remote-code\",\n \"--max-model-len\", \"1000000\",\n \"--moe-runner-backend\", \"flashinfer_cutlass\",\n \"--async-scheduling\",\n ]","new_string":" cmd = [\n \"vllm\", \"serve\", os.environ[\"MODEL_PATH\"],\n \"--served-model-name\", \"glm-5.2-nvfp4\",\n \"--host\", \"0.0.0.0\", \"--port\", \"8000\",\n \"--quantization\", \"modelopt_fp4\",\n \"--kv-cache-dtype\", \"fp8\",\n \"--tensor-parallel-size\", \"8\",\n \"--trust-remote-code\",\n \"--max-model-len\", \"1000000\",\n \"--moe-runner-backend\", \"flashinfer_cutlass\",\n \"--enable-chunked-prefill\",\n \"--max-num-batched-tokens\", \"8192\",\n \"--gpu-memory-utilization\", \"0.92\",\n \"--async-scheduling\",\n ]"}}],"created_at":"2026-06-23T05:31:44.709144Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3684,"input_tokens":825,"output_tokens":350,"cache_read_tokens":50039,"cache_creation_tokens":null,"tpot_ms":10.525714285714285,"tokens_per_sec":95.00542888165037}},{"message_id":"30b137ff-acbd-4fc9-ad7a-49a2de558b7b","node_id":73,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n144|@modal.concurrent(max_inputs=8)\n145|@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\n146|def serve():\n147| import subprocess, os\n148| cmd = [\n149| \"vllm\", \"serve\", os.environ[\"MODEL_PATH\"],\n150| \"--served-model-name\", \"glm-5.2-nvfp4\",\n151| \"--host\", \"0.0.0.0\", \"--port\", \"8000\",\n152| \"--quantization\", \"modelopt_fp4\",\n153| \"--kv-cache-dtype\", \"fp8\",\n154| \"--tensor-parallel-size\", \"8\",\n155| \"--trust-remote-code\",\n156| \"--max-model-len\", \"1000000\",\n157| \"--moe-runner-backend\", \"flashinfer_cutlass\",\n158| \"--enable-chunked-prefill\",\n159| \"--max-num-batched-tokens\", \"8192\",\n160| \"--gpu-memory-utilization\", \"0.92\",\n161| \"--async-scheduling\",\n162| ]\n163| subprocess.Popen(\" \".join(cmd), shell=True)\n164|```\n165|\n166|## What you can do","created_at":"2026-06-23T05:31:44.749289Z","finish_reason":null,"metrics":null},{"message_id":"b1352d14-cb7a-4079-b731-a10fd6b6216d","node_id":75,"role":"assistant","content":"Now update the \"What you can do\" section to remove the multi-node / disaggregation hints and the `B200+` opt-in, and add chunked-prefill tuning as a first-class lever.","tool_calls":[{"id":"chatcmpl-tool-a17e47601b10058c","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":"## What you can do\n\n- Tune `launch_server.sh` and `serve_modal.py`: quantization flags, KV cache\n dtype, TP/PP/EP split, `--max-model-len`, `--gpu-memory-utilization`,\n `--max-num-seq`, `--enable-chunked-prefill`, CUDA graphs (`--enforce-eager`\n vs `--no-enforce-eager`), `--async-scheduling`.\n- Patch vLLM source in site-packages (find with\n `python -c \"import vllm; print(vllm.__path__[0])\"`) — keep patches under\n `/app/submission/` and reapply at startup.\n- Swap the NVFP4 checkpoint (`Lorbus` vs `Mapika` vs `lukealonso`) and pin\n revisions; they differ in which layers are quantized and calibration data.\n- Re-quantize from `zai-org/GLM-5.2` with ModelOpt if no community checkpoint\n meets the correctness gate (slow; ~459 GB write).\n- Write custom Triton / CuTe DSL / FlashInfer kernels for the MoE FP4 GEMM\n or MLA attention and plug into vLLM.\n- Use a different serving backend (SGLang v0.5.13.post1+ with\n `--quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass`)\n if it beats vLLM on the benchmark — but the verifier invokes\n `launch_server.sh`, so the OpenAI-compatible HTTP interface must match.","new_string":"## What you can do\n\n- **Tune chunked prefill** — this is the primary single-node ITL lever.\n `--enable-chunked-prefill` + `--max-num-batched-tokens` controls how large\n a prefill chunk can interrupt an ongoing decode batch. Sweep it (2048 →\n 32768) against `run_dev_bench.py` to find the TTFT/ITL knee for the hidden\n workload mix.\n- Tune other vLLM scheduler flags: `--gpu-memory-utilization`,\n `--max-num-seq`, `--max-num-batched-tokens`, `--block-size`,\n `--enable-prefix-caching`, CUDA graphs (`--enforce-eager` vs\n `--no-enforce-eager`), `--async-scheduling`, `--num-scheduler-steps`.\n- Patch vLLM source in site-packages (find with\n `python -c \"import vllm; print(vllm.__path__[0])\"`) — keep patches under\n `/app/submission/` and reapply at startup.\n- Swap the NVFP4 checkpoint (`Lorbus` vs `Mapika` vs `lukealonso`) and pin\n revisions; they differ in which layers are quantized and calibration data.\n- Re-quantize from `zai-org/GLM-5.2` with ModelOpt if no community checkpoint\n meets the correctness gate (slow; ~459 GB write).\n- Write custom Triton / CuTe DSL / FlashInfer kernels for the MoE FP4 GEMM\n or MLA attention and plug into vLLM.\n- Use a different serving backend (SGLang v0.5.13.post1+ with\n `--quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass`)\n if it beats vLLM on the benchmark — but the verifier invokes\n `launch_server.sh`, so the OpenAI-compatible HTTP interface must match."}}],"created_at":"2026-06-23T05:32:02.861390Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1519,"total_time_ms":18110,"input_tokens":671,"output_tokens":858,"cache_read_tokens":50863,"cache_creation_tokens":null,"tpot_ms":19.336829836829835,"tokens_per_sec":51.71478512446507}},{"message_id":"50be53a3-6ee0-47c2-a0fe-6ee3a0b46960","node_id":76,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n162| ]\n163| subprocess.Popen(\" \".join(cmd), shell=True)\n164|```\n165|\n166|## What you can do\n167|\n168|- **Tune chunked prefill** — this is the primary single-node ITL lever.\n169| `--enable-chunked-prefill` + `--max-num-batched-tokens` controls how large\n170| a prefill chunk can interrupt an ongoing decode batch. Sweep it (2048 →\n171| 32768) against `run_dev_bench.py` to find the TTFT/ITL knee for the hidden\n172| workload mix.\n173|- Tune other vLLM scheduler flags: `--gpu-memory-utilization`,\n174| `--max-num-seq`, `--max-num-batched-tokens`, `--block-size`,\n175| `--enable-prefix-caching`, CUDA graphs (`--enforce-eager` vs\n176| `--no-enforce-eager`), `--async-scheduling`, `--num-scheduler-steps`.\n177|- Patch vLLM source in site-packages (find with\n178| `python -c \"import vllm; print(vllm.__path__[0])\"`) — keep patches under\n179| `/app/submission/` and reapply at startup.\n180|- Swap the NVFP4 checkpoint (`Lorbus` vs `Mapika` vs `lukealonso`) and pin\n181| revisions; they differ in which layers are quantized and calibration data.\n182|- Re-quantize from `zai-org/GLM-5.2` with ModelOpt if no community checkpoint\n183| meets the correctness gate (slow; ~459 GB write).\n184|- Write custom Triton / CuTe DSL / FlashInfer kernels for the MoE FP4 GEMM\n185| or MLA attention and plug into vLLM.\n186|- Use a different serving backend (SGLang v0.5.13.post1+ with\n187| `--quantization modelopt_fp4 --moe-runner-backend flashinfer_cutlass`)\n188| if it beats vLLM on the benchmark — but the verifier invokes\n189| `launch_server.sh`, so the OpenAI-compatible HTTP interface must match.\n190|\n191|## What has to stay correct\n192|\n193|The verifier runs your server and the BF16 baseline on the same hidden prompts","created_at":"2026-06-23T05:32:02.877674Z","finish_reason":null,"metrics":null},{"message_id":"0c2aa1fa-f3d0-493d-97d1-7449ef96b64f","node_id":78,"role":"assistant","content":"Now remove the `B200+` opt-in from the Constraints section.","tool_calls":[{"id":"chatcmpl-tool-bfab9d4c1a52e50d","name":"edit","arguments":{"replace_all":false,"file_path":"/Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md","old_string":"You CAN:\n\n- modify `/app/submission/launch_server.sh` and `serve_modal.py`, create\n helper files under `/app/submission`\n- modify vLLM source in site-packages (keep patches under `/app/submission`)\n- swap/pin the NVFP4 checkpoint and revision\n- install additional packages from the pre-built cache (no internet at runtime)\n- use `gpu=\"B200+\"` if your image is CUDA 13-compatible\n\nYou CANNOT:\n\n- access or reference `/tests/` or hidden verifier files\n- disable the timer daemon\n- access the internet at runtime (model must be cached or in the image/volume)\n- run on fewer than 8 B200s (weights do not fit)","new_string":"You CAN:\n\n- modify `/app/submission/launch_server.sh` and `serve_modal.py`, create\n helper files under `/app/submission`\n- modify vLLM source in site-packages (keep patches under `/app/submission`)\n- swap/pin the NVFP4 checkpoint and revision\n- install additional packages from the pre-built cache (no internet at runtime)\n\nYou CANNOT:\n\n- access or reference `/tests/` or hidden verifier files\n- disable the timer daemon\n- access the internet at runtime (model must be cached or in the image/volume)\n- run on fewer than 8 B200s (weights do not fit)\n- use a second node or disaggregated prefill/decode (single 8×B200 node only)"}}],"created_at":"2026-06-23T05:32:06.579349Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1190,"total_time_ms":3700,"input_tokens":1436,"output_tokens":379,"cache_read_tokens":51533,"cache_creation_tokens":null,"tpot_ms":6.62269129287599,"tokens_per_sec":150.99601593625496}},{"message_id":"c94ec627-3e3d-4e86-8327-722a8df90de3","node_id":79,"role":"tool","content":"The file /Users/root1/Downloads/utilities/benchmarks-prompts/glm-5.2-nvfp4-modal-b200-inference.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n216|`modal.Volume` for `/root/.cache/huggingface` so subsequent starts are fast.\n217|\n218|## Constraints\n219|\n220|You CAN:\n221|\n222|- modify `/app/submission/launch_server.sh` and `serve_modal.py`, create\n223| helper files under `/app/submission`\n224|- modify vLLM source in site-packages (keep patches under `/app/submission`)\n225|- swap/pin the NVFP4 checkpoint and revision\n226|- install additional packages from the pre-built cache (no internet at runtime)\n227|\n228|You CANNOT:\n229|\n230|- access or reference `/tests/` or hidden verifier files\n231|- disable the timer daemon\n232|- access the internet at runtime (model must be cached or in the image/volume)\n233|- run on fewer than 8 B200s (weights do not fit)\n234|- use a second node or disaggregated prefill/decode (single 8×B200 node only)\n235|\n236|## How to work\n237|\n238|```bash","created_at":"2026-06-23T05:32:06.596599Z","finish_reason":null,"metrics":null},{"message_id":"5e14698f-5477-4a6f-ae10-6102a54c5a3e","node_id":81,"role":"assistant","content":"Done. The prompt is locked to single-node 8×B200:\n\n- **Hardware section** now states the full GPU budget upfront — one vLLM engine, TP=8, collocated prefill+decode, no second node, no disaggregated pools. Explains *why* (459 GB weights need all 8 GPUs; TP=4 split leaves too little KV headroom; two TP=8 engines can't fit on 8 GPUs).\n- **Chunked prefill** is called out as the primary ITL-stability lever replacing disaggregation, with a sweep range (2048–32768) and the TTFT/ITL tradeoff explained.\n- **Reference serve command + Modal skeleton** now include `--enable-chunked-prefill`, `--max-num-batched-tokens 8192`, and `--gpu-memory-utilization 0.92`","created_at":"2026-06-23T05:32:16.818395Z","finish_reason":null,"metrics":{"ttft_ms":6246,"total_time_ms":10220,"input_tokens":null,"output_tokens":0,"cache_read_tokens":null,"cache_creation_tokens":null,"tpot_ms":0,"tokens_per_sec":0}},{"message_id":"c75658b9-733d-4287-aab8-38ef8fe74444","node_id":82,"role":"system","content":"[Response interrupted by user]","created_at":"2026-06-23T05:32:16.818479Z","finish_reason":null,"metrics":null}]}