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perf(gb300): Refresh Qwen3.5 FP4 GB300 dynamo-trt STP/MTP recipes from srt-slurm / 刷新基于 srt-slurm 的 Qwen3.5 FP4 GB300 dynamo-trt STP/MTP 配方 - #2730

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cquil11 merged 4 commits into
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Aug 27, 2026
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perf(gb300): Refresh Qwen3.5 FP4 GB300 dynamo-trt STP/MTP recipes from srt-slurm / 刷新基于 srt-slurm 的 Qwen3.5 FP4 GB300 dynamo-trt STP/MTP 配方#2730
cquil11 merged 4 commits into
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rihuo/qwen3.5-fp4-gb300-trt-recipe-refresh

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@richardhuo-nv

@richardhuo-nv richardhuo-nv commented Aug 25, 2026

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Summary / 摘要

  • Replace the qwen3.5-fp4-gb300-dynamo-trt (STP) and qwen3.5-fp4-gb300-dynamo-trt-mtp (MTP) search spaces with a new locally validated 10-point STP + 10-point MTP sweep sourced from srt-slurm (recipes/trtllm/qwen3.5-fp4/inferencex/gb300/{mtp,stp}).
  • 用来自 srt-slurm 仓库(recipes/trtllm/qwen3.5-fp4/inferencex/gb300/{mtp,stp})的新配方,替换 qwen3.5-fp4-gb300-dynamo-trt(STP)与 qwen3.5-fp4-gb300-dynamo-trt-mtp(MTP)的搜索空间,共 10 个 STP 配置点与 10 个 MTP 配置点。
  • Bump the TensorRT-LLM image 1.3.0rc181.3.0rc24, switch STP's kv-p2p-transfer from ucx to nixl to match the new recipes' NIXL cache-transceiver backend, and switch the checkpoint to nvidia/Qwen3.5-397B-A17B-NVFP4-V2.
  • 镜像升级 1.3.0rc181.3.0rc24,STP 的 kv-p2p-transferucx 切换为 nixl 以匹配新配方的 NIXL cache-transceiver 后端,并将模型切换为 nvidia/Qwen3.5-397B-A17B-NVFP4-V2
  • Append the required perf-changelog.yaml entry for both config keys.
  • 为两个 config-key 追加所需的 perf-changelog.yaml 条目。

Test plan / 测试计划

  • python3 -c "import yaml; yaml.safe_load(open('configs/nvidia-master.yaml'))" and same for perf-changelog.yaml — both parse cleanly.
  • utils/matrix_logic/generate_sweep_configs.py full-sweep --config-files configs/nvidia-master.yaml --framework dynamo-trt — generates all 20 qwen3.5 scenarios with correct topology and CONFIG_FILE mapping.
  • Full-sweep CI on this PR (label full-sweep-fail-fast applied).

🤖 Generated with Claude Code


Note

Low Risk
Benchmark and orchestration YAML only; no application logic changes. Risk is operational (wrong recipe → failed Slurm runs or misleading perf numbers).

Overview
Refreshes Qwen3.5-397B FP4 GB300 disaggregated TensorRT-LLM + Dynamo benchmark recipes and matrix entries for 8k/1k sa-bench, replacing the older rc18 / UCX-heavy sweep with a locally validated set from srt-slurm.

Runtime and checkpoint: Matrix configs move to tensorrt-llm/release:1.3.0rc24, model nvidia/Qwen3.5-397B-A17B-NVFP4-V2, and STP kv-p2p-transfer: nixl (was ucx). launch_gb300-nv.sh pins srt-slurm v1.0.72 (was v1.0.29) and still copies recipes from benchmarks/multi_node/srt-slurm-recipes/trtllm/qwen3.5/gb300-fp4/disagg.

Recipe corpus: Adds/rewrites YAML under disagg/8k1k/mtp/ and disagg/8k1k/stp/ with consistent naming (disagg-gb300-*), Dynamo wheel 1.4.0.dev20260807, staggered ctx load, and NIXL or UCX cache transceivers (max_tokens_in_buffer: 16384). Removes many legacy ctx*dep2_* rc18 configs tied to old topologies (TP2 prefill, TP16 decode, etc.).

Search space (nvidia-master.yaml): qwen3.5-fp4-gb300-dynamo-trt10 STP points (e.g. conc 42–8192, topologies from 1P2D through 24P1D). qwen3.5-fp4-gb300-dynamo-trt-mtp10 MTP points (conc 20–4301, mostly MTP draft-len 3; 26P1D uses draft-len 2; 24P1D / 8192 reuses the STP recipe as a non-MTP saturation tail). perf-changelog.yaml documents both config keys.

Reviewed by Cursor Bugbot for commit 6fff90a. Bugbot is set up for automated code reviews on this repo. Configure here.

…m srt-slurm / 刷新基于 srt-slurm 的 Qwen3.5 FP4 GB300 dynamo-trt STP/MTP 配方

Replace the qwen3.5-fp4-gb300-dynamo-trt and qwen3.5-fp4-gb300-dynamo-trt-mtp
search spaces with a new locally validated 10-point STP and 10-point MTP
sweep sourced from srt-slurm (recipes/trtllm/qwen3.5-fp4/inferencex/gb300/{mtp,stp}).
Bumps the TensorRT-LLM image to 1.3.0rc24, switches STP kv-p2p-transfer to
nixl to match the new recipes, and moves the checkpoint to
nvidia/Qwen3.5-397B-A17B-NVFP4-V2.

用来自 srt-slurm 仓库(recipes/trtllm/qwen3.5-fp4/inferencex/gb300/{mtp,stp})
的新配方,替换 qwen3.5-fp4-gb300-dynamo-trt 与 qwen3.5-fp4-gb300-dynamo-trt-mtp
的搜索空间,分别包含 10 个已本地验证的 STP 与 MTP 配置点。将 TensorRT-LLM
镜像升级至 1.3.0rc24,将 STP 的 kv-p2p-transfer 切换为 nixl 以匹配新配方,
并将模型切换到 nvidia/Qwen3.5-397B-A17B-NVFP4-V2。

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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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

Comment thread perf-changelog.yaml Outdated
- "STP: 10 configs from 1P2D (conc 42) to 24P1D (conc 8192)."
- "MTP: 10 configs from 1P2D (conc 20) to 26P1D (conc 4301, 2-token draft); the 24P1D/conc 8192 tail point shares its recipe with the STP curve, where MTP no longer helps at saturation."
- "Recipes sourced from srt-slurm (recipes/trtllm/qwen3.5-fp4/inferencex/gb300/{mtp,stp})."
pr-link: TBD

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🔴 New entry uses 'pr-link: TBD' but validate_perf_changelog.py only accepts the canonical PR URL or the literal placeholders 'XXX' / '.../pull/XXX' (PR_LINK_PLACEHOLDERS in utils/validate_perf_changelog.py:24-27).

Extended reasoning...

The perf-changelog CI gate calls validate_added_pr_link on the appended entry; since 'TBD' is neither the canonical https://github.com/SemiAnalysisAI/InferenceX/pull/ link nor in PR_LINK_PLACEHOLDERS, validate_perf_changelog.py raises ChangelogValidationError and the changelog-gate check fails on this very PR, blocking merge until the author replaces TBD with 'XXX' or the real PR link.

Verification: normal — the appended perf-changelog entry (perf-changelog.yaml diff tail) ends with pr-link: TBD, which no validation path accepts. In utils/validate_perf_changelog.py, the only accepted values are CANONICAL_PR_LINK (lines 21-23, .../pull/\d+) and PR_LINK_PLACEHOLDERS (lines 24-27, {"XXX", ".../pull/XXX"}). validate_added_pr_link (lines 144-160) raises ChangelogValidationError for "TB

model: nvidia/Qwen3.5-397B-A17B-NVFP4-V2
model-prefix: qwen3.5
runner: gb300
precision: fp4

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🟡 qwen3.5-fp4-gb300-dynamo-trt sets scenario-level kv-p2p-transfer: nixl, but 4 of the 10 new STP recipe files it references (ctx1_gen2_tep8_batch16_eplb0_mtp0_42.yaml, ctx1_gen4_tep8_batch1_eplb0_mtp0_8.yaml, ctx1_gen4_tep8_batch4_eplb0_mtp0_24.yaml, ctx4_gen3_tep8_batch64_eplb0_mtp0_210.yaml) still set cache_transceiver_config.backend: UCX internally; same pattern in qwen3.5-fp4-gb300-dynamo-trt-mtp (kv-p2p-transfer: nixl at line ~6560) where 5 of 10 mtp recipes (e.g. ctx1_gen2_tep8_batch8_eplb0_mtp3_20.yaml, ctx7_gen1_dep16_batch32_eplb0_mtp3_666.yaml) also use UCX.

Extended reasoning...

kv-p2p-transfer is a free-text metadata field (utils/matrix_logic/validation.py) surfaced in dashboards/reports as the KV transfer backend for the run, but it is never cross-checked against the CONFIG_FILE's actual cache_transceiver_config.backend. For these mismatched entries, anyone reading benchmark results or the perf-changelog will believe NIXL was used when the run actually executed with UCX, contradicting the PR's own stated goal ('switch STP's kv-p2p-transfer... to match the new recipes' NIXL cache-transceiver backend') and misleading performance comparisons/regressions attributed to NIXL vs UCX.

Verification: Severity: nit (metadata/reporting inaccuracy; nothing functionally breaks). The mismatch is real and reachable. configs/nvidia-master.yaml sets scenario-level kv-p2p-transfer: nixl for both qwen3.5-fp4-gb300-dynamo-trt (line ~6399) and qwen3.5-fp4-gb300-dynamo-trt-mtp (line ~6560). But 4 of the 10 referenced STP recipes and 5 of the 10 MTP recipes set cache_transceiver_config.backend: UCX internal

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@Ankur-singh

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/reuse-sweep-run 32901704325

@Ankur-singh

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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. Please link to GitHub Action workflow that shows this. — https://github.com/SemiAnalysisAI/InferenceX/actions/runs/32901704325
  • Verified that this PR passes evals. Please link to GitHub Action workflow that shows this. — https://github.com/SemiAnalysisAI/InferenceX/actions/runs/32901704325
  • 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, 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:
    • N/A — this PR carries no single-node vLLM/SGLang recipe.
  • 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:

  • Accepted metadata deviation: both refreshed master entries omit the Dynamo router version, and kv-p2p-transfer: nixl summarizes a mixed 11 NIXL / 9 UCX recipe set.

Signed: ankur-singh

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✅✅✅ Verdict: PASS ✅✅✅

✅ Check 0 (CODEOWNER): PASS — @Ankur-singh is a listed owner of configs/nvidia-master.yaml; remaining paths fall under the catch-all, covered by any recognized CODEOWNER.
✅ Check 1 (green sweep on in-PR commit): PASS — head 4ed3feb has all 20 multi-node 8k1k / and all 17 multi-node eval / check-runs green (executed, not skipped) in run 32901704325; multi-node PR, so single-node lanes correctly skipped.
✅ Check 2 (evals pass): PASS — 17 GSM8K results, em_strict 0.964–0.974 on Qwen3.5-397B-A17B-NVFP4-V2 with this PR's 1.3.0rc24 image.
➖ Check 3 (recipe link): N/A — exclusively disaggregated/multi-node submission (multinode: true, disagg: true, dynamo-trt, files under benchmarks/multi_node/**); the recipe-link requirement applies to single-node recipes only.
✅ Check 4 (reuse command): PASS — /reuse-sweep-run 32901704325 posted by Ankur-singh (COLLABORATOR).
✅ Check 5 (latest checklist): PASS — every current-template item present and checked, single-node recipe sub-item answered with an explicit N/A.
✅ Check 6 (upstream image / engine-first): PASS — changed entries are framework: dynamo-trt so the vLLM/SGLang upstream-image rule doesn't apply; SGLang-first ordering satisfied by existing qwen3.5-fp4-gb300-dynamo-sglang.
✅ Check 7 (deprecated models): PASS — qwen3.5 Single-turn 8k1k fp4 is active per MODELS.md as of 2026-08-27.
✅ Check 8 (architecture hacks): PASS — no hf-overrides/layer trims; native MTP speculative_config only.
✅ Check 9 (spec-decode chat template): PASS — recipes use sa-bench, whose use_chat_template defaults to true in srt-slurm v1.0.72 and is not overridden.
✅ Check 10 (engine patches): PASS — no patching; srt-slurm v1.0.29v1.0.72 is harness-side, and the Dynamo wheel install is the declared dynamo-trt framework layer used repo-wide (TRT-LLM engine runs as shipped).
➖ Check 11 (agentic golden AL): N/A — no agentic spec-decode changes; these 8k1k MTP configs run real acceptance (no synthetic-acceptance knobs added).
➖ Check 12 (append-only): N/A — the new perf-changelog entry does not set append-only: true.

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cquil11 commented Aug 27, 2026

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/stage-results 32901704325

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@cquil11 staged run 32901704325: https://inferencemax-app-git-staging-semianalysisai.vercel.app/inference?i_dates=2026-08-25~r32901704325

This run remains available across future /stage-results requests. Staging the same run ID again updates its staged data. Staging workflow

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cquil11 commented Aug 27, 2026

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/reuse-sweep-run

中文:同步 main,并恢复仅追加且字节敏感的性能变更记录。
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cquil11 merged commit 04f560c into main Aug 27, 2026
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cquil11 deleted the rihuo/qwen3.5-fp4-gb300-trt-recipe-refresh branch August 27, 2026 20:32
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