diff --git a/docs/xpu.md b/docs/xpu.md
new file mode 100644
index 0000000000..798a620abc
--- /dev/null
+++ b/docs/xpu.md
@@ -0,0 +1,220 @@
+## Intel XPU (Arc GPU) Inference
+
+OpenPI supports inference on Intel Arc GPUs (e.g. Arc B70) via the PyTorch XPU backend.
+Only the PyTorch model path is supported — JAX inference requires CUDA or CPU.
+
+---
+
+### Prerequisites
+
+1. **Intel GPU driver** — install the [Intel compute runtime](https://github.com/intel/compute-runtime/releases) (version 26.18 or later recommended).
+
+2. **Intel oneAPI Base Toolkit** — required for the SYCL/XPU runtime that PyTorch links against:
+   ```bash
+   wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
+     | gpg --dearmor \
+     | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg
+   echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" \
+     | sudo tee /etc/apt/sources.list.d/oneAPI.list
+   sudo apt update && sudo apt install intel-basekit
+   ```
+
+---
+
+### 1. Clone and switch to the XPU branch
+
+```bash
+git clone --recurse-submodules https://github.com/munikera/openpi.git
+cd openpi
+git checkout add-intel-xpu-inference
+```
+
+---
+
+### 2. Install dependencies
+
+```bash
+GIT_LFS_SKIP_SMUDGE=1 uv sync --extra xpu
+```
+
+This installs `torch==2.11.0+xpu`, `torchvision==0.26.0+xpu`, and `triton-xpu==3.7.0` from
+the PyTorch XPU index instead of the default CUDA wheels.
+
+Then apply the required transformers patches (needed for the PyTorch model path):
+
+```bash
+cp -r ./src/openpi/models_pytorch/transformers_replace/* \
+      .venv/lib/python3.11/site-packages/transformers/
+```
+
+> **Note**: you must re-run this `cp` after every `uv sync`, since sync may overwrite the
+> patched files. With uv's default hardlink mode the patch also modifies the shared
+> transformers cache. To fully undo it later run `uv cache clean transformers`.
+
+---
+
+### 3. Verify XPU is detected
+
+```bash
+uv run python -c "
+import torch
+print('torch version :', torch.__version__)
+print('xpu available :', torch.xpu.is_available())
+print('xpu device count:', torch.xpu.device_count())
+for i in range(torch.xpu.device_count()):
+    print(f'  device {i}:', torch.xpu.get_device_properties(i).name)
+"
+```
+
+Expected output on a B70:
+```
+torch version : 2.11.0+xpu
+xpu available : True
+xpu device count: 2
+  device 0: Intel(R) Graphics [0xe223]
+  device 1: Intel(R) Graphics [0xe223]
+```
+
+---
+
+### 4. Download and convert the π₀.₅ model to PyTorch
+
+The XPU backend requires a PyTorch checkpoint. The published π₀.₅-LIBERO checkpoint is JAX,
+so download and convert it (one-time, ~10 GB download).
+
+**Step 1 — download the JAX checkpoint** (no GCP credentials required):
+```bash
+uv run python -c "
+from openpi.shared.download import maybe_download
+path = maybe_download('gs://openpi-assets/checkpoints/pi05_libero')
+print('Downloaded to:', path)
+"
+```
+This caches the checkpoint to `~/.cache/openpi/openpi-assets/checkpoints/pi05_libero`.
+
+**Step 2 — convert to PyTorch:**
+```bash
+uv run examples/convert_jax_model_to_pytorch.py \
+    --checkpoint-dir ~/.cache/openpi/openpi-assets/checkpoints/pi05_libero \
+    --config-name pi05_libero \
+    --output-path checkpoints/pi05_libero_pytorch
+```
+
+**Step 3 — copy the norm stats** (the convert script only copies model weights):
+```bash
+cp -r ~/.cache/openpi/openpi-assets/checkpoints/pi05_libero/assets \
+    checkpoints/pi05_libero_pytorch/
+```
+
+The converted checkpoint is saved to `checkpoints/pi05_libero_pytorch/`.
+
+---
+
+### 5. Smoke test — verify XPU inference end-to-end
+
+**Terminal 1 — start the policy server:**
+```bash
+uv run scripts/serve_policy.py --port 8000 policy:checkpoint \
+    --policy.config pi05_libero \
+    --policy.dir checkpoints/pi05_libero_pytorch
+```
+
+The server will log autotune output for the first connection (one-time JIT compile, takes
+1–3 minutes). Wait until you see:
+```
+INFO:websockets.server:server listening on 0.0.0.0:8000
+```
+before sending any requests.
+
+> **Note:** The first `infer` call triggers `torch.compile(mode='max-autotune')` and can take
+> several minutes. The server disables WebSocket keepalive pings so the client connection stays
+> open during compilation.
+
+**Terminal 2 — send LIBERO-shaped dummy observations:**
+```bash
+uv run examples/simple_client/main.py --port 8000 --env LIBERO --num-steps 5
+```
+
+Expected output (verified on B70):
+```
+Running policy: 100%|██████████| 5/5 [00:00<00:00,  7.6it/s]
+  client_infer_ms  ~130 ms
+  policy_infer_ms  ~124 ms
+```
+
+---
+
+### 6. Run LIBERO benchmark inference on XPU
+
+The LIBERO eval requires its own environment. Initialize the submodule first if you
+haven't already:
+
+```bash
+git submodule update --init --recursive
+```
+
+**Terminal 1 — policy server** (keep running from step 5, or restart):
+```bash
+uv run scripts/serve_policy.py --port 8000 policy:checkpoint \
+    --policy.config pi05_libero \
+    --policy.dir checkpoints/pi05_libero_pytorch
+```
+
+**Terminal 2 — LIBERO client:**
+```bash
+# Create the LIBERO venv (Python 3.8 required by LIBERO)
+uv venv --python 3.8 examples/libero/.venv
+source examples/libero/.venv/bin/activate
+uv pip sync examples/libero/requirements.txt third_party/libero/requirements.txt \
+    --extra-index-url https://download.pytorch.org/whl/cu113 \
+    --index-strategy=unsafe-best-match
+uv pip install -e packages/openpi-client
+uv pip install -e third_party/libero
+export PYTHONPATH=$PYTHONPATH:$PWD/third_party/libero
+export LD_LIBRARY_PATH="$(pwd)/examples/libero/.venv/lib:$LD_LIBRARY_PATH"
+export MUJOCO_GL=osmesa
+export NUMBA_DISABLE_JIT=1
+
+# On kernels with READ_IMPLIES_EXEC hardening, clear the execstack flag from
+# the old PyTorch build to avoid "cannot enable executable stack" ImportError:
+patchelf --clear-execstack examples/libero/.venv/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so
+
+# Run libero_spatial (default suite: 10 tasks × 50 trials)
+python examples/libero/main.py --args.port 8000
+```
+
+To run a different task suite:
+```bash
+python examples/libero/main.py --args.task-suite-name libero_10
+```
+
+Available suites: `libero_spatial`, `libero_object`, `libero_goal`, `libero_10`, `libero_90`.
+
+Expected results (π₀.₅ checkpoint at 30k steps):
+
+| Libero Spatial | Libero Object | Libero Goal | Libero 10 | Average |
+|:-:|:-:|:-:|:-:|:-:|
+| 98.8% | 98.2% | 98.0% | 92.4% | 96.85% |
+
+---
+
+### Selecting a specific GPU
+
+To target a specific XPU device (e.g. when two are present), set `ZE_AFFINITY_MASK` before
+starting the server:
+
+```bash
+export ZE_AFFINITY_MASK=0   # use device 0
+uv run scripts/serve_policy.py --port 8000 policy:checkpoint \
+    --policy.config pi05_libero \
+    --policy.dir checkpoints/pi05_libero_pytorch
+```
+
+Set `ZE_AFFINITY_MASK=1` to use device 1 instead.
+
+---
+
+### Tested hardware
+
+Two Intel Arc B70 GPUs (`device 0xe223`, `xe` kernel driver, Ubuntu 22.04).
+Required VRAM for inference: > 8 GB.
diff --git a/pyproject.toml b/pyproject.toml
index 5377e3dac5..ff9c012193 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ dependencies = [
     "orbax-checkpoint==0.11.13",
     "pillow>=11.0.0",
     "sentencepiece>=0.2.0",
-    "torch==2.7.1",
+    "torch>=2.7.1",
     "tqdm-loggable>=0.2",
     "typing-extensions>=4.12.2",
     "tyro>=0.9.5",
@@ -61,14 +61,35 @@ rlds = [
     "tensorflow-cpu==2.15.0",
     "tensorflow-datasets==4.9.9",
 ]
+xpu = [
+    # Intel XPU inference support (replaces the default CUDA/CPU torch wheels).
+    # Requires Intel oneAPI Base Toolkit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html
+    "torch==2.11.0+xpu",
+    "torchvision==0.26.0+xpu",
+    "triton-xpu==3.7.0",
+]
 
 [tool.uv]
 override-dependencies = ["ml-dtypes==0.4.1", "tensorstore==0.1.74"]
 
+[[tool.uv.index]]
+name = "pytorch-xpu"
+url = "https://download.pytorch.org/whl/xpu"
+explicit = true
+
 [tool.uv.sources]
 openpi-client = { workspace = true }
 lerobot = { git = "https://github.com/huggingface/lerobot", rev = "0cf864870cf29f4738d3ade893e6fd13fbd7cdb5" }
 dlimp = { git = "https://github.com/kvablack/dlimp", rev = "ad72ce3a9b414db2185bc0b38461d4101a65477a" }
+torch = [
+    { index = "pytorch-xpu", group = "xpu" },
+]
+torchvision = [
+    { index = "pytorch-xpu", group = "xpu" },
+]
+triton-xpu = [
+    { index = "pytorch-xpu", group = "xpu" },
+]
 
 [tool.uv.workspace]
 members = ["packages/*"]
diff --git a/src/openpi/policies/policy_config.py b/src/openpi/policies/policy_config.py
index 6570df05ed..510c8dd5a7 100644
--- a/src/openpi/policies/policy_config.py
+++ b/src/openpi/policies/policy_config.py
@@ -68,7 +68,12 @@ def create_trained_policy(
         try:
             import torch
 
-            pytorch_device = "cuda" if torch.cuda.is_available() else "cpu"
+            if torch.cuda.is_available():
+                pytorch_device = "cuda"
+            elif hasattr(torch, "xpu") and torch.xpu.is_available():
+                pytorch_device = "xpu"
+            else:
+                pytorch_device = "cpu"
         except ImportError:
             pytorch_device = "cpu"
 
diff --git a/uv.lock b/uv.lock
index d7c9e410b1..6276462e57 100644
--- a/uv.lock
+++ b/uv.lock
@@ -847,6 +847,20 @@ wheels = [
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 ]
 
+[[package]]
+name = "dpcpp-cpp-rt"
+version = "2025.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "intel-opencl-rt" },
+    { name = "intel-openmp" },
+    { name = "intel-sycl-rt" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e9/d2/edcbe7995ae3fe18f709c8ae122da8978aee1311c3e8d3bdcd23fc684e69/dpcpp_cpp_rt-2025.3.2-py2.py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:7084cfa5dd43c609389b67615b212c5f5f633b44b79da1281893dbe3459f8e2d", size = 28803, upload-time = "2026-01-22T05:31:13.906Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/8b/9c9bf12d09033de5721070701b859917c0d3a2da214467caf1378a38df98/dpcpp_cpp_rt-2025.3.2-py2.py3-none-win_amd64.whl", hash = "sha256:3a8645e0ae570f54e2514c92fbfb57c0759158afe576b8c966ba06dc90729ca5", size = 58378, upload-time = "2026-01-22T05:34:29.252Z" },
+]
+
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 version = "0.10.0"
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 ]
 
+[[package]]
+name = "impi-rt"
+version = "2021.17.2"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/29/496c69c70a5645eaa2dfe230ee9ab7dcee041a64f7a6555c515512530495/impi_rt-2021.17.2-py2.py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:f10206010456d77e5b55bf19045cbc2eac46d240373539416a4fecd8a52530c8", size = 102598212, upload-time = "2026-01-22T05:30:34.604Z" },
+]
+
 [[package]]
 name = "importlib-metadata"
 version = "8.7.0"
@@ -1626,6 +1648,84 @@ wheels = [
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 ]
 
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+    { url = "https://files.pythonhosted.org/packages/de/02/46ec0e8aad41a7d95ba62dc65238c88fc302120c9a143c211990eef9c0b2/intel_cmplr_lib_rt-2025.3.2-py2.py3-none-win_amd64.whl", hash = "sha256:89bba5899d0e057f0ecbc55768558e55bd7a3740950d0e4d0b01d25d8b62b020", size = 17741766, upload-time = "2026-01-22T05:34:31.994Z" },
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+
+[[package]]
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+[[package]]
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+[[package]]
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+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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 [[package]]
 name = "ipykernel"
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