| name | xpu-deploy-plan |
| description | Plan an end-to-end Intel XPU model deployment by chaining existing skills. Calls xpu-runtime-preflight (readiness), model-can-it-fit (sizing), model-config-recommend (flags), and the selected runtime skill (vllm-xpu-run / sglang-xpu-run / torch-xpu-run), then writes a single PLAN.md with one exact launch command, smoke test, and rollback to .out/skills/xpu-deploy-plan/. Use when the user asks for a coordinated plan (not a direct deploy/serve request) — wants the orchestration across preflight, fit, config, launch, smoke test, and rollback, or asks which skills to run and in what order. |
xpu-deploy-plan
End-to-end deployment planner for Intel XPU inference. This skill is
an orchestrator only: it chains the lower-level skills, captures their
outputs, and writes a single PLAN.md for the user. It does not
duplicate runtime guidance that lives in the called skills.
Default target: Arc Pro B70 (32 GiB) home-lab host, container-first,
OpenAI-compatible HTTP serving through vLLM-XPU, unless the user's
request points elsewhere.
Before invoking the plan builder, summarize the resolved inputs back to
the user (model, runtime, target XPU, ctx, concurrency, quant) so they
can correct anything before files are written. This is courtesy, not a
safety gate — the script is read/compute only and writes a markdown
plan to .out/skills/xpu-deploy-plan/; it does not pull images, launch
containers, claim a GPU, or modify the system.
When to use
Use when the user asks for a plan — wants the orchestration, not
just to start a server:
- "Give me a deployment plan for Qwen2.5-7B on my Arc Pro B70 with vLLM."
- "Plan an end-to-end deployment of Llama-3.1-8B on my Intel GPU."
- "Which skills should I run, and in what order, to serve this model?"
- "I have two/four B70s; give me a launch plan."
Required entry point
Always invoke scripts/build_plan.sh. It is the single supported
entry point for this skill. Do not run the chained skills
(xpu-runtime-preflight, model-can-it-fit, model-config-recommend,
or the runtime launch emitters) by hand and assemble a plan yourself —
build_plan.sh already orchestrates them, captures their outputs, and
writes the canonical PLAN.md. Hand-rolled equivalents drift from the
artifact layout the rest of the toolchain expects and miss the halt
logic on a preflight FAIL.
If build_plan.sh fails or a chained skill returns a hard error,
report the failure and the routing skill from the preflight
SUMMARY.md; do not work around it by running the underlying scripts
directly.
Quick start
plugins/intel-gpu-ai-skills/skills/xpu-deploy-plan/scripts/build_plan.sh \
--model Qwen/Qwen2.5-7B-Instruct \
--runtime vllm \
--target-gpu 0 \
--ctx 4096 \
--concurrency 1 \
--out-dir .out/skills/xpu-deploy-plan
The script writes:
.out/skills/xpu-deploy-plan/PLAN.md
.out/skills/xpu-deploy-plan/inputs.json
.out/skills/xpu-deploy-plan/preflight/ # forwarded from xpu-runtime-preflight
.out/skills/xpu-deploy-plan/fit.txt # from model-can-it-fit
.out/skills/xpu-deploy-plan/config.txt # from model-config-recommend (vLLM only)
.out/skills/xpu-deploy-plan/build_plan.log
PLAN.md is the single artifact to show the user. It contains: inputs,
readiness summary, fit summary, the exact launch command from the
selected runtime skill, a smoke test that verifies XPU usage (not just
HTTP 200), the benchmark handoff, and a rollback command.
The output directory .out/skills/xpu-deploy-plan/ is the
agent-accessible artifact dir for this skill (matches the .out/ entry
in this repo's .gitignore).
Clarify only what changes the launch
If missing, ask at most three questions; otherwise use defaults.
| Question | Default |
|---|
| Which HF model id or local model path? | Required. Do not guess. |
| Runtime: vLLM / SGLang / PyTorch? | vLLM OpenAI API. |
| Target context and concurrency? | 4096 ctx, concurrency 1 for first boot. |
| Quantized weights acceptable? | BF16 if it fits; FP8/int4 only if needed. |
| How many XPUs? | Discover host; do not assume. |
Chained skills (orchestration only)
The script invokes these skills in order and HALTS on a hard failure:
- xpu-runtime-preflight — readiness gate. If
FAIL count > 0, the
plan stops with the preflight SUMMARY.md as the blocker. Do not
attempt launch.
- model-can-it-fit — VRAM sizing for the given model, ctx,
concurrency, quant, and XPU count.
- Runtime selection:
vllm (default), sglang, or torch.
- model-config-recommend — only for
vllm on Arc B-series; emits
recommended flags.
- Launch command — emitted from the selected runtime skill's
launch emitter. For vLLM, this is
vllm-xpu-run/scripts/emit_launch.sh, using the top
model-config-recommend candidate when available. Image policy,
proxy env propagation, multi-GPU oneCCL setup, and OOM recovery
live in those skills (vllm-xpu-run, sglang-xpu-run,
torch-xpu-run) and the runtime triage skill — they are NOT
duplicated here.
- Smoke test —
/v1/models, one deterministic request, plus
docker logs | grep -iE 'xpu|device|cpu fallback' and xpu-smi ps
to confirm XPU is actually used.
- Benchmark handoff — points at
vllm-xpu-bench, sglang-xpu-bench,
or torch-xpu-bench.
- Rollback —
docker stop / docker rm for containers started by the
plan. The script never removes unrelated containers or caches.
Runtime decision table
| User need | Runtime | Skill |
|---|
| OpenAI-compatible chat/completions | vLLM-XPU | vllm-xpu-run |
| Maximum current Intel model coverage | vLLM-XPU | vllm-xpu-run |
| Prefix-cache-heavy workloads | SGLang-XPU | sglang-xpu-run |
| Grammar-constrained generation | SGLang-XPU | sglang-xpu-run |
| Pure Transformers code path | PyTorch XPU | torch-xpu-run |
| Diffusion / custom HF pipeline | PyTorch XPU | torch-xpu-run |
| "How much VRAM?" | Sizing tool | model-can-it-fit |
| "Best vLLM config?" | Recommender | model-config-recommend |
Correctness gates
Bringing up the server is not the stopping point. The plan is not
"done" until the smoke test in PLAN.md passes and the agent returns
the observed response summary to the user:
- Server exposes the expected model at
/v1/models.
- One small chat/completion request returns non-empty plausible text;
quote or summarize the returned content in the final answer.
- Logs show the expected runtime/device path, not CPU fallback.
- XPU memory rises by approximately model-size scale.
- Bench numbers are captured after warmup.
What this skill does NOT cover
- Driver /
/dev/dri / group / Docker readiness — xpu-runtime-preflight.
- Low-level GPU inventory — xpu-discover.
- Detailed vLLM / SGLang / PyTorch flags, image choice, multi-GPU
oneCCL guidance, and OOM recovery — the selected runtime skill and the
runtime triage skill own those.
- Performance root-cause analysis — bench / profile skills in order:
runtime bench, runtime profile, xpu-profile-unitrace.
Reporting
In final answers, report:
- target host and target GPU id
- the readiness verdict (PASS / WARN / FAIL counts from preflight)
- the chosen runtime, image tag, and one-line launch summary
- the path to
PLAN.md for the full plan
- if
FAIL: the blocker and the routing skill from preflight SUMMARY.md
