Intel GPU AI Skills is a collection of agent skills for setting up, running, benchmarking, and profiling Hugging Face models on Intel GPUs. It supports workflows involving PyTorch, vLLM-XPU, SGLang-XPU, llama.cpp-SYCL, and migration from CUDA to XPU. The catalogue contains the project's skills, instructions, agent, and plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add intel/gpu-ai-skills --skill torch-xpu-rungit clone --depth 1 https://github.com/intel/gpu-ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/intel/gpu-ai-skills/torch-xpu-run)<a href="https://agentmods.dev/skills/intel/gpu-ai-skills/torch-xpu-run"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/torch-xpu-run/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/intel/gpu-ai-skills/torch-xpu-run"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/torch-xpu-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 89 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- high Privilege Escalation · line 90 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- high YARA Match · line 279 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium MCP Rug Pull · line 88 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00143 | $0.03731 |
| Opus 5 | $0.00072 | $0.01865 |
| Sonnet 5 | $0.00029 | $0.00746 |
| Haiku 4.5 | $0.00014 | $0.00373 |
Grade A, and why
torch-xpu-run scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
torch-xpu-run
Upstream PyTorch has a torch.xpu namespace mirroring torch.cuda
(prototype since 2.5; this skill assumes >= 2.8, which is where the
native xccl collective backend and the coverage below are dependable).
Don't use intel-extension-for-pytorch
(ipex) or ipex-llm — both are end-of-life (March 2026), upstream
PyTorch supersedes them.
CUDA -> XPU code translation
| CUDA | XPU |
|---|---|
torch.cuda.is_available() |
torch.xpu.is_available() |
torch.cuda.device_count() |
torch.xpu.device_count() |
torch.cuda.empty_cache() |
torch.xpu.empty_cache() |
torch.cuda.synchronize() |
torch.xpu.synchronize() |
torch.cuda.memory_allocated(0) |
torch.xpu.memory_allocated(0) |
model.to("cuda") |
model.to("xpu") |
tensor.to("cuda:1") |
tensor.to("xpu:1") |
with torch.autocast("cuda", torch.bfloat16) |
with torch.autocast("xpu", torch.bfloat16) |
torch.cuda.amp.GradScaler() |
torch.amp.GradScaler("xpu") — needs FP64 support, so disable it (enabled=False) on Arc A-Series, which lacks native FP64 |
device_map="auto" (Accelerate) |
same; Accelerate detects XPU directly |
dist.init_process_group(backend="nccl") |
dist.init_process_group(backend="xccl") <- only non-mechanical change |
Where to get PyTorch with XPU
XPU wheels are not on the default PyPI index. A plain
pip install torch gets the CUDA/CPU build, where torch.xpu exists
as a namespace but reports no devices. Install from the XPU index:
# stable
pip3 install torch torchvision torchaudio \
--index-url https://download.pytorch.org/whl/xpu
# nightly — only when you need an unreleased fix
pip3 install --pre torch torchvision torchaudio \
--index-url https://download.pytorch.org/whl/nightly/xpu
Pinning works the same way, e.g. pip install torch==2.11.0 torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/xpu. Take the three versions from one
release row — mixing rows breaks the ABI.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 319 lines · 143 tokens per session scan A 0de7043932c2
torch-xpu-run is a skill published in the GitHub repository intel/gpu-ai-skills (21 stars, last pushed 4d ago), licensed Apache-2.0. It adds 143 tokens to every session and 3,731 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
minicpm5-deploy-vllm-ascend
Deploy MiniCPM5-2B with vLLM on Huawei Ascend NPU using vLLM-Ascend. Use when the user mentions vLLM-Ascend, Ascend NPU, Huawei Ascend, CANN, torchnpu, davinci devices, or wants an OpenAI-compatible MiniCPM5 server on Ascend hardware.
amc-run-rtsp-calibration
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.