vLLM 서빙을 위한 Ascend NPU 추론 스택

I can't execute this setup for you — I don't have access to your repo, your Ascend NPU hardware, or the ability to run installs, check devices, or start servers. What I can do is give you the exact sequence to run yourself, with the decision points and gotchas called out so you can fill in the version-specific values from the pinned docs.

## 1. Pin down the release and read the versioned docs

- Find the pinned version in your repo (e.g. `requirements.txt`, `pyproject.toml`, `Dockerfile`, or a `VERSION` file). Note the exact `vllm` and `vllm-ascend` versions — they must match each other and the pinned release.
- Read the **versioned** docs for that release, not `main`:
  - vLLM Ascend docs: `https://docs.vllm.ai/projects/ascend/en/<version>/` (or the `vllm-ascend` GitHub release notes for that tag).
  - vLLM docs for the matching version: `https://docs.vllm.ai/en/v<version>/`.
- From those docs, extract: supported CANN version, supported PyTorch/torch-npu version, supported Python, and the exact install command (usually `pip install vllm==<x> vllm-ascend==<y>` plus a `--extra-index-url` for the Ascend wheel index).

## 2. Check the support matrix for your model

- In the vllm-ascend docs for your pinned version, find the **supported models** / **feature matrix** page.
- Confirm: (a) your model architecture is listed, (b) the quantization format (if any) is supported, (c) the parallelism you plan (TP/PP) is supported, (d) whether it needs `--enforce-eager` or specific `VLLM_ASCEND_*` env vars.
- If your model is **not** in the matrix, stop and note it — running it is unsupported and may silently produce wrong output.

## 3. Prerequisites (verify before installing)

- **CANN toolkit**: version must match what the pinned vllm-ascend release requires. Check with `cat /usr/local/Ascend/ascend-toolkit/latest/version.cfg` or `npu-smi info -t version`.
- **NPU driver/firmware**: check with `npu-smi info`. Driver version must be ≥ the minimum in the docs.
- **torch-npu**: must match the PyTorch version vllm-ascend expects.
- **OS/kernel**: some releases require specific glibc/kernel; check the docs' "environment" section.
- Source the CANN env: `source /usr/local/Ascend/ascend-toolkit/set_env.sh` (and `.../nnal/set_env.sh` if needed).

## 4. Install matching versions

```bash
pip install vllm==<pinned> vllm-ascend==<pinned>
# plus whatever extra index the versioned docs specify
```

Do **not** let pip resolve `vllm` independently — pin both explicitly so they stay in lockstep.

## 5. Verify NPU detection

```bash
npu-smi info                     # devices visible, driver OK
python -c "import torch, torch_npu; print(torch.npu.is_available(), torch.npu.device_count())"
python -c "import vllm_ascend; print(vllm_ascend.__version__)"
```

If `torch.npu.is_available()` is `False`, the problem is CANN/driver/env, not vLLM — fix that first.

## 6. Start the OpenAI-compatible server

```bash
vllm serve <your-model> \
  --device npu \
  --tensor-parallel-size <N> \
  --port 8000
# add --enforce-eager or VLLM_ASCEND_* env vars if the docs require them for your model
```

## 7. Smoke test

```bash
curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"<your-model>","prompt":"Hello","max_tokens":16}'
```

## What to report back

- Exact `vllm`, `vllm-ascend`, `torch`, `torch-npu`, CANN, and driver versions.
- Whether your model/quantization/parallelism is inside the support matrix — and if not, exactly which axis is outside it.
- Any env vars or flags the versioned docs required.

If you paste the pinned versions from your repo and the output of `npu-smi info` and the `torch.npu` check, I can tell you which doc pages to read and flag likely mismatches — but the actual install and run has to happen on your machine.