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 sglang-xpu-benchgit 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/sglang-xpu-bench)<a href="https://agentmods.dev/skills/intel/gpu-ai-skills/sglang-xpu-bench"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/sglang-xpu-bench/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/sglang-xpu-bench"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/sglang-xpu-bench.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00093 | $0.04122 |
| Opus 5 | $0.00046 | $0.02061 |
| Sonnet 5 | $0.00019 | $0.00824 |
| Haiku 4.5 | $0.00009 | $0.00412 |
Grade C, and why
sglang-xpu-bench scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
MODEL=$(docker exec "$CONTAINER_NAME" curl -s http://127.0.0.1:$SGLANG_PORT/v1/models | python3 -c "import sys,json; print(json.load(sys.stdin)['data'][0]['id'])") Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
if ! docker exec "$CONTAINER_NAME" curl -s http://127.0.0.1:$SGLANG_PORT/v1/models >/dev/null 2>&1; then How it starts
The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sglang-xpu-bench
sglang.bench_serving is SGLang's online benchmark client (the
counterpart to vllm bench serve). Speaks the OpenAI-compatible API
exposed by sglang-xpu-run.
Step 0 — verify SGLang server is running on Intel XPU
REQUIRED: Before benchmarking, you must confirm a SGLang server is running, identify its port, and verify it's using Intel XPU (not CPU fallback, not NVIDIA). This prevents benchmarking a server that silently fell back to CPU.
Run the checks below in a single shell session (later blocks reuse
$SGLANG_PID, $SGLANG_PORT, $MODEL from earlier ones).
Find the server process and its container:
# 1. Check if SGLang is running and find its container
SGLANG_PID=$(ps aux | grep -iE 'sglang|launch_server' | grep -v grep | awk 'NR==1 {print $2}')
if [ -z "$SGLANG_PID" ]; then
echo "❌ No SGLang server found running. Start one with sglang-xpu-run."
exit 1
fi
echo "✓ SGLang server found (PID: $SGLANG_PID)"
CONTAINER_NAME=$(docker ps --format '{{.Names}}' 2>/dev/null | while read name; do
if docker top "$name" -o pid 2>/dev/null | awk 'NR>1' | grep -qxF "$SGLANG_PID"; then echo "$name"; break; fi
done)
if [ -z "$CONTAINER_NAME" ]; then
echo "❌ No container matched PID $SGLANG_PID. This skill runs the bench client"
echo " via 'docker exec' because the sglang package is only installed inside"
echo " the server container. A host-only SGLang install is not supported here."
exit 1
fi
echo "✓ SGLang container: $CONTAINER_NAME"
Read the launch args once — they give you both the container-internal port
and the --device flag. The bench client runs via docker exec inside the
container, so the port must be the one SGLang binds inside the container
(host ss can't see the container-namespaced socket). The --device xpu check
is REQUIRED — it catches a server that silently fell back to CPU:
# 2. Read port + device from the launch args in one pass (REQUIRED)
ARGS=$(ps -p "$SGLANG_PID" -o args=)
SGLANG_PORT=$(echo "$ARGS" | awk '{for(i=1;i<=NF;i++) if($i=="--port" && i<NF) print $(i+1)}' | head -1)
SGLANG_PORT=${SGLANG_PORT:-30000} # sglang default
DEVICE_ARG=$(echo "$ARGS" | awk '{for(i=1;i<=NF;i++) if($i=="--device" && i<NF) print $(i+1)}' | head -1)
echo "DEVICE_ARG=${DEVICE_ARG:-none}"
if [ "$DEVICE_ARG" = "cuda" ]; then
echo "❌ Server is on NVIDIA CUDA, not Intel XPU."; exit 1
elif [ "$DEVICE_ARG" = "xpu" ]; then
echo "✓ SGLang server has --device xpu"
else
echo "⚠️ Could not confirm --device xpu (got: '${DEVICE_ARG:-none}'); relying on XPU memory check."
fi
# 3. Verify reachable from inside the container and read model name
if ! docker exec "$CONTAINER_NAME" curl -s http://127.0.0.1:$SGLANG_PORT/v1/models >/dev/null 2>&1; then
echo "❌ SGLang server not responding on container port $SGLANG_PORT."
exit 1
fi
MODEL=$(docker exec "$CONTAINER_NAME" curl -s http://127.0.0.1:$SGLANG_PORT/v1/models | python3 -c "import sys,json; print(json.load(sys.stdin)['data'][0]['id'])")
echo "✓ Responding on container port $SGLANG_PORT — model: $MODEL"
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.
- 11d ago First seen · 334 lines · 93 tokens per session scan C 1c456eb5918e
sglang-xpu-bench is a skill published in the GitHub repository intel/gpu-ai-skills (21 stars, last pushed 6d ago), licensed Apache-2.0. It adds 93 tokens to every session and 4,122 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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