[Bugfix][Kernel] Skip FlashInfer kernels that cannot run on the local CUDA toolkit by melcheikh · Pull Request #55651 · vllm-project/vllm

melcheikh · 2026-09-07T04:41:38Z

AI assistance was used on this PR. The diagnosis, the design decisions and every hardware
measurement below are mine; Claude Code (Fable 5.1) helped write the code, the tests and this
description, and ran an adversarial multi-agent review of the diff whose confirmed findings I
folded in. I have reviewed every changed line and run every command listed below on my own
machine. Attribution is in the commit trailer.

Purpose

Fixes the fused-MoE and linear-kernel paths of #50705 on SM 12.x GPUs whose local CUDA
toolkit is older than 12.9, and a same-shaped failure of FlashInfer's b12x kernels below CUDA 13.

FlashInfer needs CUDA >= 12.9 to emit code for SM 12.x. With an older toolkit its
CompilationContext logs Failed to get device capability: SM 12.x requires CUDA >= 12.9, keeps
going with an empty target-arch set, and every nvcc JIT build then fails at first use:
gen_jit_spec() raises the misleading FlashInfer requires GPUs with sm75 or higher, and the
SM 12.x-specific modules raise No supported CUDA architectures found for major versions [12].
Because vLLM auto-selects FlashInfer-backed kernels ahead of the vLLM/Triton alternatives on
SM 12.x, engine initialization dies in the profile run instead of falling back.

FlashInfer's b12x (SM 12.x CuTe-DSL) GEMM and MoE backends have the same shape of failure one
step further down the NVFP4 priority list: they require CUDA >= 13 but only check it at call time
(b12x FP4 GEMM requires CUDA 13 or later), so once the CUTLASS kernel declines, the next
candidate dies instead of the safe vLLM kernel being reached.

This adds two cached selection-time preflights to vllm/utils/flashinfer.py:

flashinfer_jit_unsupported_reason() checks that the current GPU's arch is in FlashInfer's
target set (current_compilation_context.TARGET_CUDA_ARCHS, the same object FlashInfer's own
check_cuda_arch() reads) and, when it is not, re-derives the real reason FlashInfer swallowed.
flashinfer_b12x_unsupported_reason() mirrors FlashInfer's own get_cuda_version().major < 13
check.

The selection gates consult them:

Kernel	Gate	Preflight	Next candidate in the existing priority order on SM 12.x
`FlashInferExperts` (CUTLASS fused-MoE, bf16 and fp8)	`_supports_current_device()`	JIT	`TritonExperts`
`FlashInferB12xExperts`	`_supports_current_device()`	b12x	next MoE backend
`FlashInferFP8ScaledMMLinearKernel` (per-tensor FP8 GEMM)	`is_supported()`	JIT	`CutlassFP8ScaledMMLinearKernel`
`FlashInferCutlassNvFp4LinearKernel`	`is_supported()`	JIT	`FlashInferB12xNvFp4LinearKernel`
`FlashInferB12xNvFp4LinearKernel`	`is_supported()`	b12x	`CutlassNvFp4LinearKernel` (vLLM's own)
`FlashInferCutlassMxfp8LinearKernel`	`is_supported()`	JIT	`MarlinMxfp8LinearKernel`

The reason is logged once at WARNING (FlashInfer cannot JIT-compile kernels for this GPU: SM 12.x requires CUDA >= 12.9. FlashInfer-backed kernels will not be auto-selected.). For the linear
kernels it is also what an explicit --linear-backend flashinfer_cutlass prints when it fails fast
through the kernel chooser. The MoE gates keep the bare-bool _supports_current_device() contract,
so an explicit moe_backend="flashinfer_cutlass" still fails at selection with the generic
"kernel does not support current device" message; the toolkit reason is the warning right above it.

Both probes read FlashInfer's state rather than gating on a version number, so when
flashinfer-ai/flashinfer#3633 lands (SM 12.x on CUDA 12.8) the JIT preflight passes on its own.
The JIT probe also covers a box where FlashInfer targets another GPU's arch but not this one, and a
malformed FLASHINFER_CUDA_ARCH_LIST (FlashInfer raises ValueError at import) becomes a reason
instead of a crash in kernel selection.

Why the fix is in vLLM's selection layer

The root cause is FlashInfer's (an nvcc < 12.9 cannot target compute_120f); vLLM cannot fix
that, and #3633 is the upstream fix. What vLLM owns is its priority chain: today it selects a
backend that FlashInfer has already reported it cannot compile for, and the Triton / CUTLASS
alternatives that exist are never reached. is_supported() / _supports_current_device() already
decline on compute capability and on a missing nvcc (has_flashinfer()); this adds the two
missing toolkit checks in the same place, following the approach the issue asks for and the one
used by #48956 (sampler) and #54267 (fp8 KV attention).

Relationship to the neighbouring PRs

	Failure	PR
Path 1	FlashInfer sampler	#48956 (mine, open)
Path 3	fp8 KV cache attention backend	#54267 (@Dmytro-Khvedchuk, open)
Paths 2 and 4	fused-MoE, FP8 / NVFP4 / MXFP8 linear kernels, plus the b12x CUDA 13 gap	this PR

Both open PRs inline a check_cuda_arch() probe at their own gate; this PR introduces the shared
helper they can adopt. I am happy to rebase #48956 onto it if a maintainer prefers one direction.
#54669 / #54719 (B12x MoE auto-selection) and #55170 (prefer W4A4 NVFP4 kernels on SM 12.x) change
priorities, not the toolkit gates; if #55170 lands, the CUTLASS and b12x kernels gated here become
the default NVFP4 path on SM 12.x.
#50751 documents this failure in docs/usage/troubleshooting.md; once this lands, that entry can
describe the fallback warning instead of the crash. I did not touch docs/ here to avoid colliding
with it, and can fold a note in if the reviewers prefer.

Why this is not a duplicate

gh issue view 50705 --repo vllm-project/vllm --comments
gh pr list --repo vllm-project/vllm --state open --search "50705 in:body"
gh pr list --repo vllm-project/vllm --state open --search "flashinfer moe sm120 fallback"
gh pr list --repo vllm-project/vllm --state open --search "scaled_mm flashinfer sm120"
gh pr list --repo vllm-project/vllm --state open --search "FlashInfer requires GPUs with sm75"
gh pr list --repo vllm-project/vllm --state open --search "b12x"

Nothing open touches the MoE or linear-kernel gates for this failure: #50751 is a docs PR,
#48956 / #54267 are the sampler and attention paths above, #51741 handles a sampling kernel that
fails to build after being selected, and the b12x PRs listed above are about priority order.

Test Plan

Unit tests

.venv/bin/python -m pytest tests/model_executor/kernels/test_flashinfer_jit_preflight.py \
    tests/kernels/moe/test_unquantized_backend_selection.py -q

test_flashinfer_jit_preflight.py (new, CPU-only, no FlashInfer needed) covers both preflights
against faked FlashInfer internals (arch present / absent / empty target set with the swallowed
error / import-time ValueError / FlashInfer absent / non-CUDA) and each linear-kernel gate
declining and accepting; the MoE suite gets a selection test where the unquantized oracle falls
through to Triton on an SM 12.x platform when the JIT preflight fails and keeps FlashInfer CUTLASS
when it passes, plus the b12x MoE gate. Lint: pre-commit run --files <changed files> and
pre-commit run mypy-3.12 --hook-stage manual.

End-to-end on the affected hardware (method)

RTX 5090 Laptop GPU (sm_120), driver 595.84, torch 2.11.0+cu130, flashinfer-python 0.6.18
(no flashinfer-cubin / flashinfer-jit-cache), vLLM editable at 52358e6 (stock) and at this
commit (patched). Same box, same session. The only variable between the two toolkit columns is
CUDA_HOME / PATH (nvcc 12.8.61 vs 13.1.115), which is what FlashInfer reads to decide whether it
can target SM 12.x. VLLM_USE_FLASHINFER_SAMPLER=0 in every run so the sampler path (#48956) does
not mask the result; enforce_eager=True, max_model_len=2048. Each run boots the engine and
generates one greedy completion for "The capital of France is" (e2e.py below); rc is the
process exit code.

e2e.py

import json, sys
from vllm import LLM, SamplingParams

kwargs = {"max_model_len": 2048, "gpu_memory_utilization": 0.6, "enforce_eager": True}
for kv in sys.argv[2:]:
    k, v = kv.split("=", 1)
    try:
        kwargs[k] = json.loads(v)
    except json.JSONDecodeError:
        kwargs[k] = v
llm = LLM(model=sys.argv[1], **kwargs)
out = llm.generate(["The capital of France is"], SamplingParams(max_tokens=12, temperature=0))
print("RESULT: OK", repr(out[0].outputs[0].text))

OLMoE runs used gpu_memory_utilization=0.9; NVFP4 runs used
kernel_config={"enable_flashinfer_autotune": false}.

Test Result

Unit tests and lint

48 passed, 1 skipped

pre-commit run --files <changed files> and pre-commit run mypy-3.12 --hook-stage manual pass.

RTX 5090 Laptop GPU (sm_120, 24 GB)

Model / path	stock, nvcc 12.8	patched, nvcc 12.8	patched, CUDA 13.1 (control)
`allenai/OLMoE-1B-7B-0924` (bf16, unquantized MoE)	selects `FlashInfer CUTLASS` MoE, profile run dies in `gen_cutlass_fused_moe_sm120_module`: `No supported CUDA architectures found for major versions [12]` (rc=1)	preflight warning, `TRITON` MoE backend, generates `" Paris.\n\nThe capital of the United States is Washington"` (rc=0)	selects `FlashInfer CUTLASS` MoE (preflight passes), JIT-compiles the sm120 fused-MoE module (~24 min with `MAX_JOBS=2`), generates `" Paris.\n\nThe capital of the United States is Washington"` (rc=0)
`southfreebird/Qwen2.5-0.5B-Instruct-FP8` (per-tensor static W8A8)	`Selected FlashInferFP8ScaledMMLinearKernel`, dies: `No supported CUDA architectures found for major versions [12]` (rc=1)	`Selected CutlassFP8ScaledMMLinearKernel`, generates `" Paris. It is the largest city in the world by population"` (rc=0)	`Selected FlashInferFP8ScaledMMLinearKernel`, JIT-compiles (~8 min), generates `" Paris. It is the largest city in the European Union and"` (rc=0)
`nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4`, CuTe-DSL kernels disabled so the nvcc-JIT kernels are reached (`VLLM_DISABLED_KERNELS=FlashInferCuteDslNvFp4W4A16LinearKernel,FlashInferCuteDslNvFp4LinearKernel`, FlashInfer autotune off)	`Using FlashInferCutlassNvFp4LinearKernel`, dies: `No supported CUDA architectures found for major versions [12]` (rc=1)	JIT preflight declines the CUTLASS kernel, b12x preflight declines `FlashInferB12xNvFp4LinearKernel` (`FlashInfer b12x kernels require CUDA >= 13 (local toolkit 12.8)`), `Using CutlassNvFp4LinearKernel`, generates `" Paris.\n10. What is the capital of Italy"` (rc=0)	`Using FlashInferCutlassNvFp4LinearKernel` (both preflights pass), the sm120 FP4 GEMM module JIT-compiles (~5.5 min), then the decode warmup dies in the lm_head bf16 GEMM with `CUBLAS_STATUS_INTERNAL_ERROR`. Stock `52358e6` with the same toolkit and config fails identically (with and without FlashInfer autotune), so this is a pre-existing problem of FlashInfer's CUTLASS NVFP4 kernel with nvcc 13.1 + torch cu130 on this box, not a selection change; not investigated further here

What the NVFP4 row shows without the b12x preflight (the intermediate state of this branch, kept
here because it is the evidence for gating b12x): with only the JIT preflight, the CUTLASS kernel
declines and FlashInferB12xNvFp4LinearKernel is selected next, and the engine dies at first use
with ValueError: b12x FP4 GEMM requires CUDA 13 or later. Current CUDA version: 12.8.; with the
b12x kernel disabled by hand, CutlassNvFp4LinearKernel is selected and generates
" Paris.\n10. What is the capital of Italy" (rc=0).

Two things this table does not show, stated so nobody has to discover them in review:

With the default NVFP4 priority on SM 12.x, FlashInferCuteDslNvFp4W4A16LinearKernel (CuTe-DSL,
no nvcc) is selected first for this W4A4 checkpoint, so the CUTLASS / b12x gates are not on the
default path today (they will be if [Perf][Quant][NVFP4] Prefer W4A4 linear kernels over weight-only ones on SM120/121 #55170 lands). That default path then dies in FlashInfer's
autotune of bf16_fp4_cute_dsl_gemm with a CUDA OOM, identically on stock and patched, on both the
24 GB laptop and the 96 GB RTX PRO 6000 (and with Out of bound mPartial.shape[0] under CUDA 12.9).
That is a pre-existing problem of the CuTe-DSL W4A16 path on SM 12.x, unrelated to this change;
[Bug]: NVFP4 dense linear kernel selection on SM12x picks FlashInferCuteDslNvFp4W4A16LinearKernel over the W4A4 Cutlass kernel (prefill regression on GB10) #55397 covers its priority, I will file the autotune failure separately.
FlashInferCutlassMxfp8LinearKernel and FlashInferB12xExperts were not exercised end to end
(no SM 12.x MXFP8 / b12x-MoE checkpoint at hand). They are gated because they take the identical
code paths (gen_jit_spec() calls check_cuda_arch() unconditionally; the b12x MoE runner has
the same get_cuda_version().major < 13 check), and they have the unit tests above.

Second machine: RTX PRO 6000 Blackwell Server Edition (sm_120, 96 GB)

GCP g4-standard-48 (48 vCPU, 176 GB RAM), driver 580.173.02, torch 2.13.0+cu132 (what
--torch-backend=auto resolves for that driver), flashinfer-python 0.6.18 (no cubin / jit-cache),
vLLM at this commit installed with VLLM_USE_PRECOMPILED=1. The VM's only toolkit is CUDA 12.9,
FlashInfer's minimum for SM 12.x, so it is the control column; a CUDA 12.8.1 toolkit was installed
side by side (/usr/local/cuda-12.8, default untouched) for the reproduction columns. Same
e2e.py, max_model_len=4096, VLLM_USE_FLASHINFER_SAMPLER=0, MAX_JOBS=16 for the JIT builds.
The run is unattended (bucket-driven job, logs in GCS); "stock" is the same file swap as above.

Model / path	stock, nvcc 12.8	patched, nvcc 12.8	patched, CUDA 12.9 (control)
`RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8` (per-tensor static W8A8)	`Selected FlashInferFP8ScaledMMLinearKernel`, dies: `No supported CUDA architectures found for major versions [12]` (rc=1)	`Selected CutlassFP8ScaledMMLinearKernel`, generates `" a city of grandeur and beauty, with a rich history"` (rc=0)	`Selected FlashInferFP8ScaledMMLinearKernel`, JIT ~3 min, generates `" Paris, which is also the most visited city in the world"` (rc=0)
`Qwen/Qwen3-30B-A3B` bf16 (the MoE reported in #50705)	`FlashInfer CUTLASS` MoE backend, profile run dies with the same error (rc=1)	`TRITON` MoE backend, generates `" Paris. The capital of the United Kingdom is London. The"` (rc=0)	`FlashInfer CUTLASS` MoE backend, JIT ~8 min, generates the same text (rc=0)
Gemma-4 31B it-qat NVFP4 (modelopt, in-house checkpoint), default priority	`FlashInferCuteDslNvFp4W4A16LinearKernel`, then CUDA OOM inside FlashInfer's autotune of `bf16_fp4_cute_dsl_gemm` (16384 tokens) on the 96 GB GPU (rc=1). Pre-existing: identical on stock and patched	same OOM (rc=1)	same kernel, dies with `ValueError: Out of bound mPartial.shape[0]` from the CuTe-DSL kernel (rc=1). Pre-existing, independent of this change
same Gemma-4, CuTe-DSL kernels disabled	`FlashInferCutlassNvFp4LinearKernel`, dies: `No supported CUDA architectures found for major versions [12]` (rc=1)	JIT preflight declines the CUTLASS kernel, b12x preflight declines B12x (`FlashInfer b12x kernels require CUDA >= 13 (local toolkit 12.8)`), `CutlassNvFp4LinearKernel`, generates (rc=0)	`FlashInferCutlassNvFp4LinearKernel`, JIT ~2.5 min, generates (rc=0), byte-identical output to the fallback kernel
`nvidia/Qwen3.6-35B-A3B-NVFP4` (the NVFP4 MoE from the fourth entry point in #50705). On SM 12.x its experts go to Marlin and its NVFP4 linears to `MarlinNvFp4LinearKernel`; the dense FP8 per-tensor layers (QKV / MergedColumn / RowParallel) are what select FlashInfer, so the CuTe-DSL-disabled variant behaves identically	`Selected FlashInferFP8ScaledMMLinearKernel` for the three FP8 layer types, dies: `No supported CUDA architectures found for major versions [12]` (rc=1)	`Selected CutlassFP8ScaledMMLinearKernel` for them, generates `" Paris, a city renowned for its rich history, culture,"` (rc=0)	`Selected FlashInferFP8ScaledMMLinearKernel`, generates `" Paris, a city renowned for its iconic landmarks such as the"` (rc=0)

On this box the Gemma-4 output for the raw prompt is 'thought\n---\n---\n---\n---\n---' on both
vLLM's CUTLASS kernel and FlashInfer's: an instruct model opening a thinking block on an
untemplated prompt, not a numerics difference between the kernels. The laptop's cuBLAS failure
with nvcc 13.1 did not appear with 12.9 here.

Model evaluation

Not applicable: this changes kernel selection only, and only on configurations that currently
cannot start. Where FlashInfer can compile, every gate returns exactly what it returned before
(the CUDA 13.1 column above and the accepts unit tests).

🤖 Generated with Claude Code

https://claude.ai/code/session_01FT4Q1w39aN7hSxbdfKQ8to

…olkit FlashInfer needs CUDA >= 12.9 to emit code for SM 12.x. With an older toolkit its compilation context logs "SM 12.x requires CUDA >= 12.9", keeps an empty target-arch set, and every nvcc JIT build then fails at first use: "FlashInfer requires GPUs with sm75 or higher" from generic specs, "No supported CUDA architectures found for major versions [12]" from the SM 12.x modules. vLLM auto-selects FlashInfer-backed kernels ahead of the vLLM/Triton ones on SM 12.x, so engine initialization dies in the profile run instead of falling back (vllm-project#50705, fused-MoE and linear-kernel paths). FlashInfer's b12x kernels fail the same way one step further down the NVFP4 priority list: they require CUDA >= 13 but only check at call time. Add two cached selection-time preflights in vllm/utils/flashinfer.py: flashinfer_jit_unsupported_reason(), which checks that the current GPU's arch is in FlashInfer's target set and re-derives the swallowed error, and flashinfer_b12x_unsupported_reason(), which mirrors FlashInfer's own CUDA >= 13 check. Consult them from the selection gates of FlashInferExperts, FlashInferB12xExperts, FlashInferFP8ScaledMMLinearKernel, FlashInferCutlassNvFp4LinearKernel, FlashInferB12xNvFp4LinearKernel and FlashInferCutlassMxfp8LinearKernel. The existing priority order then reaches Triton MoE, CUTLASS FP8 or vLLM's CUTLASS NVFP4 kernel, the real reason is logged once at WARNING, and an explicit --linear-backend override fails fast with it. Signed-off-by: martin el cheikh <martinelcheikh@gmail.com> Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FT4Q1w39aN7hSxbdfKQ8to

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coderabbitai · 2026-09-07T04:42:38Z

📝 Summary

Summary by CodeRabbit

Bug Fixes
- Improved GPU compatibility checks for FlashInfer-accelerated kernels.
- Automatically falls back to supported alternatives when FlashInfer JIT compilation or specialized kernels are unavailable.
- Added clearer compatibility diagnostics for unsupported GPU architectures and CUDA versions.
Tests
- Added coverage for FlashInfer preflight checks and backend selection across supported and unsupported configurations.

Walkthrough

FlashInfer now checks CUDA architecture and toolkit compatibility before enabling JIT-dependent and b12x kernels. Kernel support gates use these diagnostics, and MoE backend selection falls back to Triton when FlashInfer CUTLASS is unsupported.

Changes

FlashInfer compatibility preflight

Layer / File(s)	Summary
Preflight diagnostics `vllm/utils/flashinfer.py`, `tests/model_executor/kernels/test_flashinfer_jit_preflight.py`	Adds cached diagnostics for FlashInfer JIT targets and b12x CUDA version requirements. Tests cover unsupported platforms, missing targets, import failures, malformed configuration, and CUDA version parsing.
Kernel support gates `vllm/model_executor/kernels/linear/...`, `vllm/model_executor/layers/fused_moe/experts/...`	Linear kernels and fused MoE experts reject unsupported JIT or b12x environments and return diagnostic reasons.
Kernel and selection validation `tests/model_executor/kernels/test_flashinfer_jit_preflight.py`, `tests/kernels/moe/test_unquantized_backend_selection.py`	Tests validate linear and b12x support gates and verify fallback from `FLASHINFER_CUTLASS` to `TRITON` when JIT preflight fails.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🔵 Low · up to 65c22

The changed NVFP4 error message can fail the required lint check, blocking validation until it is rewritten with explicit concatenation.

Sequence Diagram(s)

sequenceDiagram
  participant MoEBackendSelection
  participant FlashInferCutlassMoe
  participant FlashInferDiagnostics
  participant Triton
  MoEBackendSelection->>FlashInferCutlassMoe: check device support
  FlashInferCutlassMoe->>FlashInferDiagnostics: request JIT compatibility reason
  FlashInferDiagnostics-->>FlashInferCutlassMoe: None or unsupported reason
  alt JIT supported
    FlashInferCutlassMoe-->>MoEBackendSelection: select FLASHINFER_CUTLASS
  else JIT unsupported
    FlashInferCutlassMoe-->>MoEBackendSelection: reject backend
    MoEBackendSelection->>Triton: select TRITON fallback
  end

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Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 31.58% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 38 functions across 8 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

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Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly and concisely describes the main change: preventing FlashInfer kernel selection when the local CUDA toolkit cannot support execution.
Description check	✅ Passed	The description directly explains the selection-time preflights, affected kernels, fallback behavior, tests, and validation results.

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Inline comments:
In `@vllm/model_executor/kernels/linear/nvfp4/flashinfer.py`:
- Around line 404-405: Update the FlashInfer b12x error message string near
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adjacent string literals, preserving the existing message text.

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tests/kernels/moe/test_unquantized_backend_selection.py
tests/model_executor/kernels/test_flashinfer_jit_preflight.py
vllm/model_executor/kernels/linear/mxfp8/flashinfer.py
vllm/model_executor/kernels/linear/nvfp4/flashinfer.py
vllm/model_executor/kernels/linear/scaled_mm/flashinfer.py
vllm/model_executor/layers/fused_moe/experts/flashinfer_b12x_moe.py
vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
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melcheikh requested review from AndreasKaratzas, WoosukKwon, mgoin, pavanimajety, tlrmchlsmth, yewentao256 and zyongye as code owners September 7, 2026 04:41

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melcheikh marked this pull request as draft September 7, 2026 04:42

melcheikh changed the title ~~[Bugfix] Skip FlashInfer kernels that cannot run on the local CUDA toolkit~~ [Bugfix][Kernel] Skip FlashInfer kernels that cannot run on the local CUDA toolkit Sep 7, 2026

melcheikh marked this pull request as ready for review September 7, 2026 04:47

melcheikh mentioned this pull request Sep 7, 2026

fix(jit): refresh stale arch detection and allow SM120 on CUDA 12.8 flashinfer-ai/flashinfer#3633

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[Bugfix][Kernel] Skip FlashInfer kernels that cannot run on the local CUDA toolkit - #55651

[Bugfix][Kernel] Skip FlashInfer kernels that cannot run on the local CUDA toolkit#55651
melcheikh wants to merge 1 commit into
vllm-project:mainfrom
melcheikh:fix-flashinfer-jit-preflight-moe-scaled-mm

melcheikh commented Sep 7, 2026 •

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coderabbitai Bot commented Sep 7, 2026 •

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Summary by CodeRabbit

Walkthrough

Changes

Sequence Diagram(s)

❌ Failed checks (1 warning)

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github-actions Bot commented Sep 7, 2026

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melcheikh commented Sep 7, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Purpose

Why the fix is in vLLM's selection layer

Relationship to the neighbouring PRs

Why this is not a duplicate

Test Plan

Unit tests

End-to-end on the affected hardware (method)

Test Result

Unit tests and lint

RTX 5090 Laptop GPU (sm_120, 24 GB)

Second machine: RTX PRO 6000 Blackwell Server Edition (sm_120, 96 GB)

Model evaluation

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Claude Code Review

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coderabbitai Bot commented Sep 7, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Summary by CodeRabbit

Walkthrough

Changes

Sequence Diagram(s)

❌ Failed checks (1 warning)

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github-actions Bot commented Sep 7, 2026

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melcheikh commented Sep 7, 2026 •

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coderabbitai Bot commented Sep 7, 2026 •

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