A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 13, 15:45 UTC
[doc] add guide about the provided PTX was compiled with an unsupported toolchain (#28305)
Signed-off-by: youkaichao <youkaichao@gmail.com>
youkaichao committed
Nov 8, 2025 at 00:26 UTC
155ad56d7b567441685a8f8ebf51b7077e68e054
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docs/usage/troubleshooting.md
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@@ -316,6 +316,10 @@ Traceback (most recent call last):
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This indicates vLLM failed to initialize the NCCL communicator, possibly due to a missing `IPC_LOCK` linux capability or an unmounted `/dev/shm`. Refer to [Enabling GPUDirect RDMA](../serving/parallelism_scaling.md#enabling-gpudirect-rdma) for guidance on properly configuring the environment for GPUDirect RDMA.
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+## CUDA error: the provided PTX was compiled with an unsupported toolchain
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+If you see an error like `RuntimeError: CUDA error: the provided PTX was compiled with an unsupported toolchain.`, it means that the CUDA PTX in vLLM's wheels was compiled with a toolchain unsupported by your system. The released vLLM wheels have to be compiled with a specific version of CUDA toolkit, and the compiled code might fail to run on lower versions of CUDA drivers. Read [cuda compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/) for more details. The solution is to install `cuda-compat` package from your package manager. For example, on Ubuntu, you can run `sudo apt-get install cuda-compat-12-9`, and then add `export LD_LIBRARY_PATH=/usr/local/cuda-12.9/compat:$LD_LIBRARY_PATH` to your `.bashrc` file. When successfully installed, you should see that the output of `nvidia-smi` will show `CUDA Version: 12.9`. Note that we use CUDA 12.9 as an example here, you may want to install a higher version of cuda-compat package in case vLLM's default CUDA version goes higher.
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## Known Issues
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- In `v0.5.2`, `v0.5.3`, and `v0.5.3.post1`, there is a bug caused by [zmq](https://github.com/zeromq/pyzmq/issues/2000) , which can occasionally cause vLLM to hang depending on the machine configuration. The solution is to upgrade to the latest version of `vllm` to include the [fix](https://github.com/vllm-project/vllm/pull/6759).