# Qwen/Qwen3.5-0.8B — vLLM serving recipe

> Qwen3.5 tiny dense multimodal model (0.8B) — ultra-low-VRAM / edge serving with 262K context

Configuration: hardware `h200` (8× H200) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (BF16) · features `tool_calling`, `reasoning` · frontend `rust`

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/Qwen/Qwen3.5-0.8B
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/Qwen/Qwen3.5-0.8B.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`, `arc_pro_b60`, `arc_pro_b70`
- `variant`: `default`
- `strategy`: `single_node_tp` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `tool_calling`, `reasoning`, `text_only` (default: `tool_calling`, `reasoning`)

## Install

Requires vLLM >= 0.17.0.

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

Docker image: `vllm/vllm-openai:latest`

## Serve

```bash
export VLLM_USE_RUST_FRONTEND=1
vllm serve Qwen/Qwen3.5-0.8B \
  --trust-remote-code \
  --tensor-parallel-size 1 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3
```

Docker equivalent:

```bash
docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e VLLM_USE_RUST_FRONTEND=1 \
  vllm/vllm-openai:latest Qwen/Qwen3.5-0.8B \
  --trust-remote-code \
  --tensor-parallel-size 1 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

[Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) is the smallest
member of the Qwen3.5 family — same hybrid gated delta networks architecture
and 262K context, at a size suited to edge devices or as a draft model for
speculative decoding with larger Qwen3.5 checkpoints.

### Prerequisites

- **vLLM version:** >= 0.17.0
- **Hardware:** any modern GPU (>=4 GB VRAM) or Intel Arc Pro B60/B70

#### Install vLLM

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend=auto
```

#### Docker
```bash
docker pull vllm/vllm-openai-xpu:latest  # Intel XPU (B60 / B70)
```

### Launching the Server

```bash
vllm serve Qwen/Qwen3.5-0.8B \
  --max-model-len 262144 \
  --reasoning-parser qwen3
```

#### Docker (Intel XPU B60 / B70)

Validated on 1× Intel Arc Pro B60 / B70 (B60 24 GB, B70 32 GB per card) with the
official vLLM XPU image `vllm/vllm-openai-xpu:latest`.

```bash
docker run --device /dev/dri \
  -v /dev/dri/by-path:/dev/dri/by-path --shm-size=16g \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai-xpu:latest \
  Qwen/Qwen3.5-0.8B \
  --reasoning-parser qwen3 \
  --enforce-eager
```

### Client Usage

```python
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
resp = client.chat.completions.create(
    model="Qwen/Qwen3.5-0.8B",
    messages=[{"role": "user", "content": "Hi!"}],
    max_tokens=64,
)
print(resp.choices[0].message.content)
```

### References

- [Model card](https://huggingface.co/Qwen/Qwen3.5-0.8B)
- [Base checkpoint](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base)