[Docker][Hotfix] CUDA compatibility enablement by emricksini-h · Pull Request #32474 · vllm-project/vllm

emricksini-h · 2026-01-16T13:47:55Z

A change in #30784 was introduced to ensure CUDA compatibility libraries remained available even after an ldconfig cache update (which happens when apt-get is called for example). However, this caused a regression on systems where compatibility libraries are not required (when the host driver is newer than the container's CUDA version), as reported in #32373.

This PR reverts the change to /etc/ld.so.conf.d and implements a safer strategy by appending the compatibility path to LD_LIBRARY_PATH. This ensures that compatibility libraries persist after an ldconfig cache reset but are only used as a fallback.

Fixes #32373

gemini-code-assist

Code Review

This pull request is a hotfix to address a regression related to CUDA compatibility libraries in the Docker build. The changes revert a persistent modification to the system's dynamic linker configuration, opting for a safer strategy of appending the compatibility library path to the LD_LIBRARY_PATH environment variable. This ensures the libraries are available as a fallback without causing issues on systems where they are not needed. My review identifies a potential issue in the Dockerfile where an environment variable is not quoted, which could lead to build failures under certain conditions. I've provided suggestions to improve the robustness of the script.

docker/Dockerfile

emricksini-h · 2026-01-16T13:52:43Z

cc @wangshangsam & @huydhn.
Let me know if you prefer to do it in 2 PRs (yours #32377 + one adding the LD_LIBRARY_PATH, or this one doing the two).

…reset on `apt-get` (vllm-project#30784)" This reverts commit 2a60ac9. Signed-off-by: emricksini-h <emrick.birivoutin@hcompany.ai>

Signed-off-by: emricksini-h <emrick.birivoutin@hcompany.ai>

wangshangsam · 2026-01-16T19:19:16Z

@emricksini-h did you test this change and it worked for you?

I'll test this branch on CUDA 13. If this works, let's merge this PR.

Could you also add Fixes #32373 in your PR description?

emricksini-h · 2026-01-16T22:12:23Z

Yes, I've tested on my side and it's working as expected.

wangshangsam · 2026-01-17T09:15:09Z

Hmmm ... weirdly enough ... the current main (specifically, 8e61425ee6d0bd03d3669c148eba8b263d101273) works for me on both GH200 and GB200 just fine:
GH200:

root@0234b6d6eac5:/vllm-workspace# ldconfig -p | grep cuda
	libnvrtc.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc.so.13
	libnvrtc-builtins.so.13.0 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc-builtins.so.13.0
	libnvidia-ptxjitcompiler.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-ptxjitcompiler.so.1
	libnvidia-nvvm70.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm70.so.4
	libnvidia-nvvm.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so.4
	libnvidia-nvvm.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so
	libnvidia-gpucomp.so.580.82.07 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-gpucomp.so.580.82.07
	libnvblas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvblas.so.13
	libicudata.so.70 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libicudata.so.70
	libcusparse.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusparse.so.12
	libcusolverMg.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolverMg.so.12
	libcusolver.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolver.so.12
	libcurand.so.10 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so.10
	libcurand.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so
	libcudart.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so.13
	libcudart.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so
	libcudadebugger.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcudadebugger.so.1
	libcudadebugger.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcudadebugger.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcuda.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so.1
	libcuda.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so
	libcublasLt.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublasLt.so.13
	libcublas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublas.so.13
root@0234b6d6eac5:/vllm-workspace# python3 -c 'import torch; torch.cuda.is_available()'

GB200:

root@nvl72122-T18:/vllm-workspace# ldconfig -p | grep cuda
	libnvrtc.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc.so.13
	libnvrtc-builtins.so.13.0 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc-builtins.so.13.0
	libnvidia-ptxjitcompiler.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-ptxjitcompiler.so.1
	libnvidia-nvvm70.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm70.so.4
	libnvidia-nvvm.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so.4
	libnvidia-nvvm.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so
	libnvidia-gpucomp.so.580.82.07 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-gpucomp.so.580.82.07
	libnvblas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvblas.so.13
	libicudata.so.70 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libicudata.so.70
	libcusparse.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusparse.so.12
	libcusolverMg.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolverMg.so.12
	libcusolver.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolver.so.12
	libcurand.so.10 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so.10
	libcurand.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so
	libcudart.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so.13
	libcudart.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so
	libcudadebugger.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcudadebugger.so.1
	libcudadebugger.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcudadebugger.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcuda.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so.1
	libcuda.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so
	libcublasLt.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublasLt.so.13
	libcublas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublas.so.13
root@nvl72122-T18:/vllm-workspace# python3 -c 'import torch; torch.cuda.is_available()'

However, this PR works for me on GH200 but NOT GB200:
GH200:

root@604913cbe23b:/vllm-workspace# ldconfig -p | grep cuda
	libnvrtc.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc.so.13
	libnvrtc-builtins.so.13.0 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc-builtins.so.13.0
	libnvblas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvblas.so.13
	libicudata.so.70 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libicudata.so.70
	libcusparse.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusparse.so.12
	libcusolverMg.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolverMg.so.12
	libcusolver.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolver.so.12
	libcurand.so.10 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so.10
	libcurand.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so
	libcudart.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so.13
	libcudart.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so
	libcudadebugger.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcudadebugger.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcuda.so.1
	libcublasLt.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublasLt.so.13
	libcublas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublas.so.13
root@604913cbe23b:/vllm-workspace# python3 -c 'import torch; torch.cuda.is_available()'

GB200:

root@nvl72122-T18:/vllm-workspace# ldconfig -p | grep cuda
	libnvrtc.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc.so.13
	libnvrtc-builtins.so.13.0 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc-builtins.so.13.0
	libnvblas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvblas.so.13
	libicudata.so.70 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libicudata.so.70
	libcusparse.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusparse.so.12
	libcusolverMg.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolverMg.so.12
	libcusolver.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolver.so.12
	libcurand.so.10 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so.10
	libcurand.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so
	libcudart.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so.13
	libcudart.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so
	libcudadebugger.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcudadebugger.so.1
	libcuda.so.1 (libc6,AArch64) => /usr/lib/aarch64-linux-gnu/libcuda.so.1
	libcublasLt.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublasLt.so.13
	libcublas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublas.so.13
root@nvl72122-T18:/vllm-workspace# python3 -c 'import torch; torch.cuda.is_available()'
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 803: system has unsupported display driver / cuda driver combination (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:119.)
  return torch._C._cuda_getDeviceCount() > 0

I can only assume the difference between what @huydhn saw vs. what I saw on main is caused by some environment differences between the machines we are using, but I thought that the share libraries are entirely contained within the image. @huydhn were you building another image on top of the base vLLM image, which installs CUDA 12.9 or something?
But yeah, as far as this PR is concerned, I don't think we can merge it as-is.

huydhn · 2026-01-19T20:47:03Z

Once the Docker image from this PR is ready, I can try to run a round of benchmark with it and report back

wangshangsam · 2026-01-20T19:21:07Z

Once the Docker image from this PR is ready, I can try to run a round of benchmark with it and report back

@huydhn I uploaded the two images to:

wangshangsam · 2026-01-24T21:23:45Z

Actually, in one of the internal gb300 dev machines we have (which is different from the one I was using previously), I am seeing this problem appearing on main, so I guess this is indeed machine-dependent.

root@a98df7abfc4e:/vllm-workspace# ldconfig -p | grep cuda                                                                                                                                                                                  [2/1909]
        libnvrtc.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc.so.13                                                                                                                                                     
        libnvrtc-builtins.so.13.0 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc-builtins.so.13.0                                                                                                                               
        libnvidia-ptxjitcompiler.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-ptxjitcompiler.so.1
        libnvidia-nvvm70.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm70.so.4
        libnvidia-nvvm.so.4 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so.4
        libnvidia-nvvm.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-nvvm.so
        libnvidia-gpucomp.so.580.82.07 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libnvidia-gpucomp.so.580.82.07
        libnvblas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libnvblas.so.13
        libicudata.so.70 (libc6,AArch64) => /lib/aarch64-linux-gnu/libicudata.so.70
        libcusparse.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusparse.so.12
        libcusolverMg.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolverMg.so.12
        libcusolver.so.12 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcusolver.so.12
        libcurand.so.10 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so.10
        libcurand.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcurand.so
        libcudart.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so.13
        libcudart.so (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcudart.so
        libcudadebugger.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcudadebugger.so.1
        libcudadebugger.so.1 (libc6,AArch64) => /usr/lib/libcudadebugger.so.1
        libcuda.so.1 (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so.1
        libcuda.so.1 (libc6,AArch64) => /usr/lib/libcuda.so.1
        libcuda.so (libc6,AArch64) => /usr/local/cuda-13.0/compat/libcuda.so
        libcuda.so (libc6,AArch64) => /usr/lib/libcuda.so
        libcublasLt.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublasLt.so.13
        libcublas.so.13 (libc6,AArch64) => /usr/local/cuda/targets/sbsa-linux/lib/libcublas.so.13
root@a98df7abfc4e:/vllm-workspace# python3 -c 'import torch; torch.cuda.is_available()'
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 803: system has unsupported display driver / cuda driver combination (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:119.)
  return torch._C._cuda_getDeviceCount() > 0
root@a98df7abfc4e:/vllm-workspace# nvidia-smi 
Sat Jan 24 13:21:24 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GB300                   On  |   00000008:06:00.0 Off |                    0 |
| N/A   26C    P0            172W / 1400W |       0MiB / 284208MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+
|   1  NVIDIA GB300                   On  |   00000009:06:00.0 Off |                    0 |
| N/A   25C    P0            172W / 1400W |       0MiB / 284208MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+
|   2  NVIDIA GB300                   On  |   00000018:06:00.0 Off |                    0 |
| N/A   25C    P0            168W / 1400W |       0MiB / 284208MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+
|   3  NVIDIA GB300                   On  |   00000019:06:00.0 Off |                    0 |
| N/A   25C    P0            172W / 1400W |       0MiB / 284208MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |

@emricksini-h could you change this PR to simply just reverting your previous PR? And let's just get it merged to prevent further breakage for now.

This reverts commit 6a77794.

This reverts commit c4d639c.

emricksini-h · 2026-01-28T10:19:28Z

Closing this in favor of #33116. It resolves the same initial issue but implements a better approach by lowering the priority of the CUDA compatibility libs. This allows the system to prioritize newer CUDA drivers when available, avoiding the initialization errors we saw here.

mergify bot added ci/build nvidia labels Jan 16, 2026

github-project-automation bot added this to NVIDIA Jan 16, 2026

emricksini-h changed the title ~~[Hotfix] CUDA compat~~ [Docker][Hotfix] CUDA compatibility enablement Jan 16, 2026

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emricksini-h added 2 commits January 16, 2026 14:54

Revert "[Improvement] Persist CUDA compat libraries paths to prevent …

4a7d454

…reset on `apt-get` (vllm-project#30784)" This reverts commit 2a60ac9. Signed-off-by: emricksini-h <emrick.birivoutin@hcompany.ai>

Add compat libs path to LD_LIBRARY_PATH

c4d639c

Signed-off-by: emricksini-h <emrick.birivoutin@hcompany.ai>

emricksini-h force-pushed the hotfix/cuda-compat branch from 820b0be to c4d639c Compare January 16, 2026 13:55

Add quotes

6a77794

Signed-off-by: emricksini-h <emrick.birivoutin@hcompany.ai>

huydhn approved these changes Jan 16, 2026

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wangshangsam assigned emricksini-h Jan 16, 2026

emricksini-h requested a review from huydhn January 19, 2026 13:42

wangshangsam mentioned this pull request Jan 20, 2026

[Bug]: Fail to load vLLM on new NVIDIA driver #32373

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emricksini-h added 2 commits January 26, 2026 13:47

Revert "Add quotes"

5b60a2b

This reverts commit 6a77794.

Revert "Add compat libs path to LD_LIBRARY_PATH"

986929e

This reverts commit c4d639c.

emricksini-h closed this Jan 28, 2026

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[Docker][Hotfix] CUDA compatibility enablement#32474

[Docker][Hotfix] CUDA compatibility enablement#32474
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