vllm - ✅(Solved) Fix [Bug]: gdn prefill kernel errors [1 pull requests, 16 comments, 4 participants]

vllm2026-03-18 01:50:20

ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

GitHub issue URL

Helpful · Quick feedback

GitHub stats

vllm-project/vllm#37365•Fetched 2026-04-08 00:53:16

View on GitHub

Comments

Participants

Timeline

Reactions

Author

Participants

Timeline (top)

commented ×16mentioned ×3subscribed ×3cross-referenced ×1

Fix Action

Fix / Workaround

NUMA node0 CPU(s): 0-47,96-143 NUMA node1 CPU(s): 48-95,144-191 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

PR fix notes

PR #37507: [Bugfix] Fall back to Triton/FLA when system CUDA toolkit < 12.6 for GDN prefill kernel

Repository: vllm-project/vllm
Author: yanghui1-arch
State: closed | merged: False
Link: https://github.com/vllm-project/vllm/pull/37507

Description (problem / solution / changelog)

Purpose

Fix #37365 #35725 .

When running Qwen3.5 on SM90 GPUs (H100/H20/H800) with a system CUDA toolkit older than 12.6, vLLM crashes at first inference with JIT compilation errors:

namespace 'cuda::ptx' has no member 'tensormap_replace_global_dim'
namespace 'cuda::ptx' has no member 'n32_t'
namespace 'cuda::ptx' has no member 'fence_proxy_tensormap_generic'

Root cause

The existing guard in ChunkGatedDeltaRule.__init__ only checks the GPU compute capability:

supports_flashinfer = (
    current_platform.is_cuda() and current_platform.is_device_capability(90)
)

This is insufficient. A user can have an SM90 GPU with CUDA 12.4 installed — passing the capability check — while the FlashInfer GDN prefill kernel fails to JIT-compile.

FlashInfer officially supports CUDA 12.6, 12.8, 13.0, 13.1 (FlashInfer GPU/CUDA support matrix). The FlashInfer GDN prefill kernel (flat_collective_store.hpp) uses the cuda::ptx namespace from libcudacxx, which was first introduced in CCCL 2.3.0 — shipped with CUDA 12.4. However CCCL 2.3.0 cuda::ptx doesn't have tensormap_replace_global_dim, n32_t and fence_proxy_tensormap_generic until 2.4.0 — shipped with CUDA 12.5. Since FlashInfer's minimum supported toolkit is 12.6, that is the correct and officially-grounded threshold.

Fix

At model init time, probe the system nvcc version (using the same lookup order as FlashInfer's own JIT: CUDA_HOME → which nvcc → CUDA_PATH). If the toolkit is older than 12.6, disable the FlashInfer GDN kernel and fall back to Triton/FLA automatically, with a clear warning:

WARNING: FlashInfer GDN prefill kernel requires CUDA toolkit >= 12.6
(the minimum CUDA version supported by FlashInfer), but system nvcc is
12.4. Falling back to Triton/FLA.
Use --gdn-prefill-backend triton to suppress this warning.

Users can also permanently suppress the check by setting --gdn-prefill-backend triton.

Test Plan

This fix is a startup-time guard that selects between two already-existing execution paths. No new code path is introduced.

To verify the fallback behaviour manually on a system with CUDA toolkit < 12.6 and an SM90 GPU:

# Confirm the warning is emitted and inference succeeds via Triton/FLA
CUDA_VISIBLE_DEVICES=0 python -m vllm.entrypoints.openai.api_server \
  --model <path-to-Qwen3.5-27B> \
  --tensor-parallel-size 1 \
  --enable-prefix-caching \
  --max_model_len 8192

# Expected log output:
# WARNING - FlashInfer GDN prefill kernel requires CUDA toolkit >= 12.6 ..., Falling back to Triton/FLA.
# INFO - Using Triton/FLA GDN prefill kernel

To verify FlashInfer is still selected on CUDA >= 12.6:

# On a system with nvcc >= 12.6 and SM90 GPU:
CUDA_VISIBLE_DEVICES=0 python -m vllm.entrypoints.openai.api_server \
  --model <path-to-Qwen3.5-27B> \
  --tensor-parallel-size 1

# Expected log output:
# INFO - Using FlashInfer GDN prefill kernel

Test Result

Before this fix (SM90 GPU + CUDA 12.4):

RuntimeError: CUDA error during JIT compilation of GDN prefill kernel:
  .../flashinfer/flat/hopper/collective/flat_collective_store.hpp(251):
  error: namespace 'cuda::ptx' has no member 'tensormap_replace_global_dim'
  .../flat_collective_store.hpp(256):
  error: namespace 'cuda::ptx' has no member 'n32_t'
  .../flat_collective_store.hpp(262):
  error: namespace 'cuda::ptx' has no member 'fence_proxy_tensormap_generic'

Server fails to serve any requests.

After this fix (SM90 GPU + CUDA 12.4):

WARNING - FlashInfer GDN prefill kernel requires CUDA toolkit >= 12.6
(the minimum CUDA version supported by FlashInfer), but system nvcc is
12.4. Falling back to Triton/FLA. Use --gdn-prefill-backend triton to
suppress this warning.
INFO - Using Triton/FLA GDN prefill kernel

Server starts and serves requests correctly via the Triton/FLA backend.

<details> <summary> Essential Elements of an Effective PR Description Checklist </summary>

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

</details>

Changed files

vllm/model_executor/models/qwen3_next.py (modified, +46/-0)

Code Example

CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-5.15.0-141-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.4.99
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e
GPU 4: NVIDIA H20-3e
GPU 5: NVIDIA H20-3e
GPU 6: NVIDIA H20-3e
GPU 7: NVIDIA H20-3e

Nvidia driver version        : 550.163.01
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================

NUMA node0 CPU(s):                    0-47,96-143
NUMA node1 CPU(s):                    48-95,144-191
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusp
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0+cu129             pypi_0    pypi
[conda] torchvision               0.25.0+cu129             pypi_0    pypi
[conda] transformers              4.57.6                   pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.17.2rc1.dev3+g20b14095a (git sha: 20b14095a)
vLLM Build Flags:
  CUDA Archs: 9.0; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
NIC0    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      PIX     SYS     SYS
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     PIX      X      SYS     SYS
NIC2    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS      X      PIX
NIC3    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     PIX      X 

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3

==============================
     Environment Variables
==============================
TORCH_CUDA_ARCH_LIST=9.0
LD_LIBRARY_PATH=/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib/python3.12/site-packages/nvidia/nvjitlink/lib:/root/anaconda3/envs/vllm-qwen35/lib/python3.12/site-packages/nvidia/nvidia/nvjitlink/lib:/usr/local/cuda-12.4/lib64:/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib:/usr/local/cuda-12.4/lib64:/usr/local/cuda-12.4/lib64:
CUDA_HOME=/usr/local/cuda-12.4
CUDA_HOME=/usr/local/cuda-12.4
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root



----------

---

CUDA_VISIBLE_DEVICES=2,3  python -m vllm.entrypoints.openai.api_server --tensor-parallel-size 2  --model /data/models/Qwen3.5-27B --served-model-name Qwen3.5-27B --api-key xxx --reasoning-parser qwen3   --enable-prefix-caching  --tool-call-parser qwen3_coder --enable-auto-tool-choice --gpu-memory-utilization 0.9 --max_model_len 262144  --port 8060

RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>

CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-5.15.0-141-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.4.99
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e
GPU 4: NVIDIA H20-3e
GPU 5: NVIDIA H20-3e
GPU 6: NVIDIA H20-3e
GPU 7: NVIDIA H20-3e

Nvidia driver version        : 550.163.01
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================

NUMA node0 CPU(s):                    0-47,96-143
NUMA node1 CPU(s):                    48-95,144-191
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusp
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0+cu129             pypi_0    pypi
[conda] torchvision               0.25.0+cu129             pypi_0    pypi
[conda] transformers              4.57.6                   pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.17.2rc1.dev3+g20b14095a (git sha: 20b14095a)
vLLM Build Flags:
  CUDA Archs: 9.0; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     NODE    NODE    48-95,144-191   1               N/A
NIC0    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      PIX     SYS     SYS
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     PIX      X      SYS     SYS
NIC2    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS      X      PIX
NIC3    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     PIX      X 

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3

==============================
     Environment Variables
==============================
TORCH_CUDA_ARCH_LIST=9.0
LD_LIBRARY_PATH=/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib/python3.12/site-packages/nvidia/nvjitlink/lib:/root/anaconda3/envs/vllm-qwen35/lib/python3.12/site-packages/nvidia/nvidia/nvjitlink/lib:/usr/local/cuda-12.4/lib64:/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib:/root/anaconda3/envs/vllm-qwen35/lib:/usr/local/cuda-12.4/lib64:/usr/local/cuda-12.4/lib64:
CUDA_HOME=/usr/local/cuda-12.4
CUDA_HOME=/usr/local/cuda-12.4
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root



----------

</details>

🐛 Describe the bug

run

CUDA_VISIBLE_DEVICES=2,3  python -m vllm.entrypoints.openai.api_server --tensor-parallel-size 2  --model /data/models/Qwen3.5-27B --served-model-name Qwen3.5-27B --api-key xxx --reasoning-parser qwen3   --enable-prefix-caching  --tool-call-parser qwen3_coder --enable-auto-tool-choice --gpu-memory-utilization 0.9 --max_model_len 262144  --port 8060

flashinfer report some gdn prefill kernel errors, can not inference successfully

Before submitting a new issue...

Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

extent analysis

Fix Plan

To resolve the GDN prefill kernel errors and successfully run the inference, follow these steps:

Update the TORCH_CUDA_ARCH_LIST environment variable to include the architecture of the GPUs being used (in this case, H20-3e which corresponds to CUDA architecture 9.0 or 9.2 for Ampere-based GPUs, but H20-3e specifically requires 9.0 for compatibility with CUDA 12.4).
Ensure that the CUDA version installed matches the one required by the PyTorch version. In this case, PyTorch was built with CUDA 12.9, but the system has CUDA 12.4 installed. This mismatch might cause compatibility issues.
Adjust the gpu-memory-utilization parameter to a lower value (e.g., 0.8) to prevent running out of GPU memory, which could cause kernel launch failures.
Verify that the model and data are correctly loaded and that there are no issues with the --model path or the model itself.

Example code adjustments:

import os

# Before running the script
os.environ['TORCH_CUDA_ARCH_LIST'] = '9.0'

# Command to run the script with adjusted parameters
CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server --tensor-parallel-size 2  --model /data/models/Qwen3.5-27B --served-model-name Qwen3.5-27B --api-key xxx --reasoning-parser qwen3   --enable-prefix-caching  --tool-call-parser qwen3_coder --enable-auto-tool-choice --gpu-memory-utilization 0.8 --max_model_len 262144  --port 8060

Verification

To verify that the fix worked:

Check the flashinfer logs for any remaining GDN prefill kernel errors.
Test the inference with a small input to ensure it runs successfully.
Monitor GPU memory usage to ensure it does not exceed the specified utilization threshold.

Extra Tips

Ensure all GPUs used have the same architecture to avoid compatibility issues.
Keep CUDA and cuDNN versions up to date and compatible with PyTorch.
Regularly clean up temporary files and check disk space to prevent storage-related issues.

Vote matrix · Quick signals

Works

Did the solution work? Tap to confirm.

Easy Fix

Was it a quick fix?

Time Saver

Did it save you time?

Blocking

Was it severely blocking?

Common Issue

Are others likely hitting this too?

Flaky / Intermittent

Is it intermittent?

Verified / Reproducible

Can you reproduce it reliably?

#api #ssr #installation #tensor shape #environment variable #parallel task #integration issue #index setup

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Data

Security

Network

Code

UI/UX

Text

System

Multimedia

Protocol

API

Engineering

vllm - ✅(Solved) Fix [Bug]: gdn prefill kernel errors [1 pull requests, 16 comments, 4 participants]

Recommended Tools

GitHub issue graph ai analysis

Fix Action

Fix / Workaround

PR fix notes

PR #37507: [Bugfix] Fall back to Triton/FLA when system CUDA toolkit < 12.6 for GDN prefill kernel

Description (problem / solution / changelog)

Purpose

Root cause

Fix

Test Plan

Test Result

Changed files

Code Example

Your current environment

🐛 Describe the bug

Before submitting a new issue...

extent analysis

Fix Plan

Verification

Extra Tips

Still need to ship something?

TRENDING

vllm - ✅(Solved) Fix [Bug]: gdn prefill kernel errors [1 pull requests, 16 comments, 4 participants]

Recommended Tools

GitHub issue graph ai analysis

Fix Action

Fix / Workaround

PR fix notes

PR #37507: [Bugfix] Fall back to Triton/FLA when system CUDA toolkit < 12.6 for GDN prefill kernel

Description (problem / solution / changelog)

Purpose

Root cause

Fix

Test Plan

Test Result

Changed files

Code Example

Your current environment

🐛 Describe the bug

Before submitting a new issue...

extent analysis

Fix Plan

Verification

Extra Tips

Still need to ship something?

RELATED_DISCOVERY

TRENDING