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 6, 20:30 UTC
[XPU][Doc] Remove manual OneAPI install step, now handled by torch-xpu (#36831)
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Kunshang Ji committed
Mar 12, 2026 at 09:46 UTC
513949f95f3d0dd1c4d5843b6b8291b2531ad31c
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docs/getting_started/installation/gpu.xpu.inc.md
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@@ -7,7 +7,6 @@ vLLM initially supports basic model inference and serving on Intel GPU platform.
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--8<-- [start:requirements]
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- Supported Hardware: Intel Data Center GPU, Intel ARC GPU
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-- OneAPI requirements: oneAPI 2025.3
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- Dependency: [vllm-xpu-kernels](https://github.com/vllm-project/vllm-xpu-kernels): a package provide all necessary vllm custom kernel when running vLLM on Intel GPU platform,
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- Python: 3.12
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!!! warning
@@ -26,8 +25,8 @@ Currently, there are no pre-built XPU wheels.
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--8<-- [end:pre-built-wheels]
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--8<-- [start:build-wheel-from-source]
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-- First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers) and [Intel OneAPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) 2025.3 or later.
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-- Second, install Python packages for vLLM XPU backend building:
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+- First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers).
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+- Second, install Python packages for vLLM XPU backend building (Intel OneAPI dependencies are installed automatically as part of `torch-xpu`, see [PyTorch XPU get started](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html)):
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```bash
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git clone https://github.com/vllm-project/vllm.git