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Update MiniMax-M3 GB200 aggregate recipes / 更新 MiniMax-M3 GB200 聚合配方 - #2925

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Oseltamivir merged 5 commits into
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Sep 10, 2026
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Update MiniMax-M3 GB200 aggregate recipes / 更新 MiniMax-M3 GB200 聚合配方#2925
Oseltamivir merged 5 commits into
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config/minimax-m3-gb200-nightly-native

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@RohitNagraj

@RohitNagraj RohitNagraj commented Sep 9, 2026

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Description / 描述

  • Update the MiniMax-M3 GB200 aggregate configuration with single-node TP4 resident, single-node TP4 lazy SimpleCPU KV offload, and two-node TP8 resident variants.

  • Pin the serving stack to vllm/vllm-openai:nightly-9ea8f3ffc354901b740f0b31988900897b7221d7, Dynamo 1.5.0.dev20260908, and NVIDIA/srt-slurm d50ee7280c33d469df8708e363e23be2456e94fb.

  • Use worker-native minimax_m3 tool and reasoning parsing, EAGLE3 with FlashInfer, and FULL_AND_PIECEWISE CUDA graphs.

  • Wait for the OpenAI chat route before AgentX trace replay and explicitly enable chat templating in all three speculative-decoding recipes.

  • 更新 MiniMax-M3 GB200 聚合配置,加入单节点 TP4 常驻、单节点 TP4 延迟 SimpleCPU KV 卸载和双节点 TP8 常驻三种部署方式。

  • 将服务栈固定为 vllm/vllm-openai:nightly-9ea8f3ffc354901b740f0b31988900897b7221d7、Dynamo 1.5.0.dev20260908 和 NVIDIA/srt-slurm d50ee7280c33d469df8708e363e23be2456e94fb

  • 使用 worker 原生的 minimax_m3 工具与推理解析器、基于 FlashInfer 的 EAGLE3,以及 FULL_AND_PIECEWISE CUDA 图模式。

  • 在 AgentX 轨迹回放前等待 OpenAI chat 路由就绪,并在三个推测解码配方中显式启用聊天模板。

Validation / 验证

  • Exact-key generation emits six configurations with the intended topology / 精确 key 生成产生六个具有预期拓扑的配置
  • All three recipes pass srtctl dry-run at the pinned revision / 三个配方均在固定版本上通过 srtctl dry-run
  • Master/recipe image, runtime, topology, parser, speculative-decoding, and chat-template invariants pass / master 与配方的镜像、运行时、拓扑、解析器、推测解码和聊天模板不变量检查通过
  • YAML parsing, Bash syntax, changelog validation, matrix tests, and AgentX/eval tests pass / YAML 解析、Bash 语法、变更日志验证、矩阵测试及 AgentX/eval 测试通过

Type of change / 变更类型

  • Configuration change / 配置变更
  • Runtime integration change / 运行时集成变更

Note

Low Risk
Benchmark and CI configuration only—pinned images, recipes, and launcher clone logic—with no application auth or production serving code changes.

Overview
Refreshes MiniMax-M3 GB200 aggregate AgentX by adding three new srt-slurm recipes (single-node TP4 GPU-resident, single-node TP4 with lazy SimpleCPU KV offload, two-node TP8 resident) and rewiring minimaxm3-fp4-gb200-dynamo-vllm-agentic-agg-mtp in nvidia-master.yaml to use them.

The stack moves from vLLM v0.27.1 to a pinned vLLM nightly, Dynamo 1.5.0.dev20260908, and srt-slurm commit d50ee728… (GB200 launcher checks out that SHA instead of v1.0.50). Recipes enable Dynamo KV routing, worker minimax_m3 reasoning/tool parsers, EAGLE3 speculative decoding, FULL_AND_PIECEWISE CUDA graphs, use_chat_template: true, and synthetic acceptance env vars; the simple-offload variant adds kv-transfer-config / VLLM_USE_SIMPLE_KV_OFFLOAD.

Search space now includes no-offload TP4 and two-node TP8 points, adjusts dram-utilization and concurrency for the vLLM-simple offload arm, and points CONFIG_FILE at the new YAML paths. perf-changelog.yaml documents the change.

Reviewed by Cursor Bugbot for commit 2b0b640. Bugbot is set up for automated code reviews on this repo. Configure here.

更新 MiniMax-M3 GB200 聚合配方,固定服务运行时,并加入 TP4 常驻、TP4 延迟 SimpleCPU KV 卸载和双节点 TP8 常驻配置。
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github-actions Bot commented Sep 9, 2026

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Thanks for the contribution! Please reach out to respective companies' CODEOWNER to fill in the latest PR_REVIEW_CHECKLIST.md before pinging core maintainer on Slack for review. In order for the signoff PR check bot to trigger, you must follow the PR_REVIEW_CHECKLIST.md template correctly, including the phrase As a PR reviewer and CODEOWNER, I have reviewed this and have.

For PR verification, add the full-sweep-fail-fast label (strongly recommended) to this PR — the benchmark sweep only runs on labeled PRs. Use full-sweep-enabled only if you need matrix jobs to keep running past a failure.

PR authors are responsible for ensuring that after merging, all GitHub Action jobs fully pass. A lot of the time, failures are just flakes and simply re-running the failed jobs will fix it. See GitHub's docs on re-running failed jobs


感谢你的贡献!请联系相应公司的 CODEOWNER 填写最新的 PR_REVIEW_CHECKLIST.md,然后再在 Slack 上联系核心维护者进行审阅。为了触发 signoff PR 检查机器人,你必须正确遵循 PR_REVIEW_CHECKLIST.md 模板,包括保留英文语句 As a PR reviewer and CODEOWNER, I have reviewed this and have

如需进行 PR 验证,请为此 PR 添加 full-sweep-fail-fast 标签(强烈推荐)— 基准测试 sweep 仅在带有标签的 PR 上运行。仅当需要矩阵任务在失败后继续运行时才使用 full-sweep-enabled

PR 作者有责任确保合并后所有 GitHub Action 任务完全通过。 很多时候失败只是偶发抖动(flake),重新运行失败的任务即可解决。参见 GitHub 关于重新运行失败任务的文档

将 MiniMax-M3 GB200 变更日志条目关联到对应的拉取请求。

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I reviewed this PR and didn't find any bugs. Because it changes multi-node cluster launch behavior (srt-slurm checkout strategy) and a benchmark script's readiness-wait gating shared across ~100+ recipes, a human look would still be worthwhile.

What was reviewed:

  • configs/nvidia-master.yaml search-space changes cross-checked against the three new recipe YAMLs (image/container, router version, topology fields all match).
  • Confirmed model.container equals the pinned image in all three new nightly-native recipe YAMLs.
  • Checked the EVAL_ONLY guard removal in agentic_srt.sh and its updated test — the test now asserts the readiness wait runs unconditionally, matching the code.
  • perf-changelog.yaml entry appended at file tail without altering existing bytes; still carries a placeholder pull/XXX link.
Extended reasoning...

Overview

This PR reworks the MiniMax-M3 GB200 FP4 aggregate agentic-coding benchmark: it trims the search space in configs/nvidia-master.yaml to three topology arms (TP4 resident, TP4 with vllm-simple DRAM KV offload, and two-node TP8 resident), adds three new 'nightly-native' recipe YAMLs, pins a new vLLM nightly image and Dynamo router version, changes the srt-slurm checkout strategy in runners/launch_gb200-nv.sh from a shared branch-pinned shallow clone to an independent full-clone-then-checkout of a specific commit, removes an EVAL_ONLY conditional gating a readiness wait in benchmarks/multi_node/agentic_srt.sh, and updates/renames a corresponding unit test.

Security risks

None identified. This is benchmark configuration and CI/orchestration tooling; no user input, auth, or crypto paths are touched. The git clone/checkout change fetches from a fixed public GitHub URL and pins by commit SHA verified via git rev-parse HEAD, which is arguably safer than the prior branch-based pin.

Level of scrutiny

The bug-hunting system ran to a dry streak (i.e., exhausted its search without finding further issues) and reported no bugs, with two candidate concerns (the always-on readiness wait's blast radius across shared recipes, and the full-clone vs. scoped-clone divergence from sibling model paths) investigated and explicitly ruled out. The changes are mechanical and pattern-following (config/recipe pins, changelog append, test rename), but they touch a shared script (agentic_srt.sh) used by many other models' recipes and a cluster launcher script, which is why I lean toward a human co-reviewing rather than a confident stand-alone approval, even absent concrete findings.

Other factors

The test coverage for the EVAL_ONLY removal was updated appropriately and follows the repo's test-quality guidance (no frozen image/pin literals baked into new assertions beyond what's already an existing pattern for this test file). The perf-changelog entry's pull/XXX placeholder is a known pre-merge TODO already flagged as a convention item, not a functional bug.

This review covers commit 0cf6543, which is no longer the latest commit on this pull request; later commits are not covered by it.

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@kedarpotdar-nv

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/reuse-sweep-run 34384638294

@kedarpotdar-nv

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As a PR reviewer and CODEOWNER, I have reviewed this and have:

  • Verified that as of the moment of typing this, this is the latest version of PR_REVIEW_CHECKLIST.md
  • Verified that the general code quality meets the InferenceX standard and does not make the code quality any worse.
  • Verified that this PR has passed PR validation. https://github.com/SemiAnalysisAI/InferenceX/actions/runs/34384638294
  • Verified that this PR passes evals. https://github.com/SemiAnalysisAI/InferenceX/actions/runs/34384638294
  • Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
  • For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in golden_al_distribution/ for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
  • Verified against the current MODELS.md that this PR does not submit a deprecated model, scenario, or model-scenario combination.
  • Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
  • If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet as supported by vLLM/SGLang community maintainers
  • If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL8, Rubin NVL72, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.
  • Verified that every single-node vLLM/SGLang recipe in this PR is documented in the official vLLM recipes and/or the SGLang cookbook:
    • I linked the corresponding upstream PR in the vLLM recipe repo or SGLang repo and verified that it is MERGED before this InferenceX PR merges. An opened, draft, or closed-without-merge upstream PR does not satisfy this requirement. If the matching recipe was already published, I linked the published recipe/cookbook page in the additional detail section below.
  • Verified that this PR does not patch the inference engine or serving stack — the pinned image must run as shipped. This covers .patch files / git apply / patch, inline patches embedded in benchmark scripts (e.g. a python3/sed heredoc that rewrites installed engine sources before serving), in-place edits of site-packages, monkey-patching, overwriting container files, and installing forked/rebuilt engine wheels on top of the pinned image. The only exception is a patch covered by a filled-out waiver at docs/waiver/<PR_NUMBER>.md — named after the PR that introduces the patch and filed in that same PR, stating what is patched, why the unmodified upstream image cannot run this benchmark, the upstream PR/issue link, and the removal plan — which I have linked below in the additional detail section.
  • If this PR uses append-only: true, verified that it only adds generated points or recipe variants inside a selected existing config/scenario and existing same-image visual curve: every previously generated point remains present with the same recipe, no prior point is removed or rerun, and every benchmark-affecting change in the complete diff can affect only the corresponding newly appended points (never an existing point), regardless of which file contains it.
  • If any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.

Additional detail section:

  • Validation and eval run: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/34384638294
  • Authorized reuse: /reuse-sweep-run 34384638294
  • All six benchmark points and six eval points passed. GSM8K em_strict ranged from 0.9469 to 0.9560, above the 0.94 bar.
  • This is an exclusively multi-node/aggregate submission, so the upstream single-node recipe-link requirement is not applicable.
  • All three recipes use the upstream vllm/vllm-openai:nightly-9ea8f3ffc354901b740f0b31988900897b7221d7 image.
  • All three recipes set use_chat_template: true and wait for the OpenAI chat route before AgentX replay.
  • The EAGLE3-GQA recipes use three speculative tokens with thinking enabled and synthetic acceptance length 2.78, matching golden_al_distribution/minimaxm3_eagle3_gqa.yaml. Eval-only runs restore real block verification.
  • This updates an existing Dynamo-vLLM GB200 configuration; it does not introduce a new additional-framework configuration.
  • This PR does not patch the serving engine and does not use append-only: true.

Signed: kedarpotdar-nv

@Klaud-Cold

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✅✅✅ Verdict: PASS ✅✅✅

✅ Check 0 (CODEOWNER): PASS — kedarpotdar-nv is a listed owner of configs/nvidia-master.yaml; remaining paths are catch-all-only, covered by a recognized CODEOWNER.
✅ Check 1 (sweep on in-PR commit): PASS — head c91e35c (in PR) has all 6 multi-node agentic / + 6 multi-node agentic eval / check-runs executed with success in run 34384638294; other lanes correctly skipped (no configs selected).
✅ Check 2 (evals pass): PASS — downloaded eval_results_all: 6/6 GSM8K points, em_strict 0.9469–0.9560, all above the minimaxm3 bar (default 0.90 in utils/evals/thresholds.yaml), run on the PR's exact image vllm/vllm-openai:nightly-9ea8f3ffc....
➖ Check 3 (recipe link): N/A — disaggregated/multi-node submission (all recipes under benchmarks/multi_node/srt-slurm-recipes/**, multinode: true, dynamo-vllm); the recipe-link requirement applies to single-node recipes only.
✅ Check 4 (reuse command): PASS — /reuse-sweep-run 34384638294 posted by kedarpotdar-nv (COLLABORATOR).
✅ Check 5 (latest checklist): PASS — sign-off contains every item of the current docs/PR_REVIEW_CHECKLIST.md template, all checked.
✅ Check 6 (upstream image / engine-first): PASS — image is upstream vllm/vllm-openai:nightly-9ea8f3ffc354901b740f0b31988900897b7221d7; PR only updates the existing minimaxm3-fp4-gb200-dynamo-vllm-agentic-agg-mtp entry, adds no new additional-framework entry (upstream-vLLM minimaxm3-fp4-b200-vllm-agentic-mtp also exists).
✅ Check 7 (deprecated models): PASS — MODELS.md keeps minimaxm3 agentic coding active with the EAGLE3 arm published; this PR is exactly that arm.
✅ Check 8 (architecture hacks): PASS — no --hf-overrides/config edits; language-model-only is the repo-standard text-only-benchmark flag (skips loading the unused vision encoder, no FLOPs removed), fp8 kv/indexer dtype is precision-only with passing evals.
✅ Check 9 (chat templates): PASS — all three recipes set use_chat_template: true and agentic_srt.sh now waits for the OpenAI chat route before every replay.
✅ Check 10 (engine patches): PASS — no patches; Dynamo 1.5.0.dev20260908 install and the NVIDIA/srt-slurm pin are harness/frontend components, the pinned vLLM image runs as shipped.
✅ Check 11 (golden AL): PASS — SYNTHETIC_ACCEPTANCE_LENGTH=2.78 on all three agentic arms matches golden_al_distribution/minimaxm3_eagle3_gqa.yaml (thinking_on, 3 spec tokens → 2.78; recipes use num_speculative_tokens: 3, thinking:true); inject_synthetic_acceptance.py sets rejection_sample_method: synthetic for throughput and restores real block verification for eval-only; no synthetic knobs on non-agentic configs.
➖ Check 12 (append-only): N/A — the new perf-changelog entry does not use append-only: true.

@Oseltamivir Oseltamivir left a comment

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lgtm

Resolve conflicts in configs/nvidia-master.yaml and perf-changelog.yaml.

- nvidia-master: keep this branch's rewrite of
  minimaxm3-fp4-gb200-dynamo-vllm-agentic-agg-mtp (nightly image, TP4
  resident / TP4 lazy SimpleCPU offload / two-node TP8 resident, the
  -nightly-native recipe files), which supersedes main's PR #2807 tuning
  of the same entry. Main's disagg-mtp rewrite and the new Qwen3.5 GB300
  Pareto entries are retained unchanged.
- perf-changelog: keep both sides; main's #2812/#2807/#2623 entries stay
  in place and this branch's #2925 entry is appended at the end.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

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Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.

Fix All in Cursor

❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.

Reviewed by Cursor Bugbot for commit 6aa4605. Configure here.

Comment thread benchmarks/multi_node/agentic_srt.sh Outdated
合并 main 并解决变更日志冲突。
恢复仅在评测模式下等待 AgentX 端点。
@Oseltamivir
Oseltamivir merged commit 34bc0cc into main Sep 10, 2026
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@Oseltamivir
Oseltamivir deleted the config/minimax-m3-gb200-nightly-native branch September 10, 2026 18:03
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