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xpu: add XPU to additional pages #2076

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12 changes: 12 additions & 0 deletions _additional_platforms/xpu.json
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{
"name": "XPU",
"support_channel": "https://github.com/pytorch/pytorch",
"stable": {
"linux": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu",
"windows": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu"
},
"preview": {
"linux": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu",
"windows": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu"
}
}
56 changes: 56 additions & 0 deletions _get_started/additional_platforms/xpu.md
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# Installing on Intel GPU (XPU) Platform

XPU is a PyTorch device backend designed to support hardware acceleration on Intel GPUs. Key technical features:

* Native support for FP32, BF16, FP16, and Automatic Mixed Precision (AMP)
* Extensions of operator set through custom SYCL kernels
* Graph compilation
* Distributed training (through `XCCL`)
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@riverliuintel riverliuintel Jun 8, 2026 •
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we can either keep it simple, similar to how we present computes on the primary page—with a very brief introduction and a redirect to our getting started pages—or, alternatively, we can highlight major features directly in this section.

XPU brings native Intel GPU support to PyTorch with a growing set of upstreamed capabilities, enabling performant training and inference on both Linux and Windows:

  • Supports both eager and graph execution
  • Enables training and inference workflows
  • Broad operator coverage and model readiness
  • Built-in support for FP32, BF16, FP16, FP8 and AMP delivers improved performance and memory efficiency
  • Scales across devices with distributed training via the XCCL backend
  • Supports PyTorch CPP Extension API through SYCL-based custom kernels


## Prerequisites

### Hardware Requirements

* Intel Client GPU:

* Intel® Arc A-Series Graphics (CodeName: Alchemist)
* Intel® Arc B-Series Graphics (CodeName: Battlemage)
* Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (CodeName: Meteor Lake-H)
* Intel® Core™ Ultra Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Arrow Lake-H)
* Intel® Core™ Ultra Mobile Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Lunar Lake)
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* Intel® Core™ Ultra Mobile Processors (Series 3) with Intel® Arc™ Graphics (CodeName: Panther Lake)

* Intel Data Center GPU:

* Intel® Data Center GPU Max Series (CodeName: Ponte Vecchio)

### Software Requirements

* [Intel GPU Driver](https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu.html)
* Python 3.10 or later

## Installation

### pip

Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.

## Verification

To ensure that PyTorch was installed correctly with XPU support, run the following code:

```python
import torch
print(torch.__version__)

# Check XPU availability
if torch.xpu.is_available():
print("XPU is available!")
print(f"XPU devices: {torch.xpu.device_count()}")
else:
print("XPU is not available.")
```

## Documentation

For more information, please visit the [Getting Started on Intel GPU](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).