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When CODEOWNER from the respective hardware AI chip company is reviewing & approving their respective PRs, please fill in the following form in your approval comment before pinging an core maintainer for final approval
Only one eligible CODEOWNER reviewer needs to post the checklist for each PR. Check for an existing checklist before posting; additional reviewers do not need to post their own copies. For corrections, missing evidence, or verification retries, the original reviewer must edit their existing checklist comment instead of adding a new one. Create a replacement only if the original comment was deleted.
We welcome InferenceX partners and the community to submit PRs that make reasonable additions to or deletions from this checklist, provided they follow the principles of InferenceX. The general principle is that deleting a guideline should be as easy as adding one.
We also welcome InferenceX partners and the ML community to improve codeowner-signoff-verify.yml, the CI bot that independently verifies these sign-offs, and make it more rigorous too.
The automated publisher rejects incomplete or inconsistent verifier output. Each check must appear once, and the overall verdict must agree with the check results; otherwise, retry verification.
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](https://github.com/SemiAnalysisAI/InferenceX/blob/main/inferencex-e2e/docs/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.
- [ ] Verified that this PR passes evals. Please link to GitHub Action workflow that shows this.
- [ ] Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
- [ ] Verified that every draft model and draft head is served as it ships: the draft that ships with the served checkpoint, at its stored precision, through the pinned upstream image's default handling, with the shipped and effective draft precision recorded in the additional detail section. No submission-side quantization, dtype override, checkpoint substitution, or patch may lower draft precision below that default, regardless of eval results or AL. Explicitly verified that `SGLANG_NVFP4_CKPT_FP8_NEXTN_MOE` is not enabled in the effective recipe, including inherited settings; enabling it is prohibited going forward, and historical runs do not grant an exception. See [Draft-model precision](https://github.com/SemiAnalysisAI/InferenceX/blob/main/CONTRIBUTING.md#draft-model-precision) for what counts as the default and the MLPerf comparison.
- [ ] 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 [infx/golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/inferencex-e2e/infx/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](https://github.com/SemiAnalysisAI/InferenceX/blob/main/inferencex-e2e/docs/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; target/verifier FLOPs at lower precisions is fine, given that the config passes private evals, but this does not permit lowering draft-model or draft-head precision below what ships. 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 NVL72, 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](https://recipes.vllm.ai/) and/or the [SGLang cookbook](https://docs.sglang.io/cookbook/intro):
- [ ] I linked the corresponding upstream PR in the [vLLM recipe repo](https://github.com/vllm-project/recipes) or [SGLang repo](https://github.com/sgl-project/sglang/tree/main/docs_new) 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/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/inferencex-e2e/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.
- [ ] Reported measured throughput/E2EL Pareto counts and evidence per affected curve (≥5 points strongly recommended). Below 5 or unverifiable: tag a core maintainer for review; recorded admin bypass required before merge. N/A if no curves are affected. [Details](https://github.com/SemiAnalysisAI/InferenceX/blob/main/.github/codeowner-signoff-verify-prompt.md#check-14--pareto-coverage-recommendation-with-admin-exception).
### Additional detail section:
- insert any additional info here
Signed: `FILL_IN_GITHUB_USERNAME`
