Qwen/Qwen3.5-2B

Qwen3.5 mini dense multimodal model (2B) — edge / low-VRAM serving with 262K context

Edge-scale Qwen3.5 dense — fits on 8 GB GPUs or 1x Intel Arc Pro B60/B70

View on HuggingFace

dense2B262,144 ctxvLLM 0.17.0+multimodaltext

Guide

Overview

Qwen3.5-2B is a miniature dense Qwen3.5 model — the full gated delta networks architecture, vision encoder, and 262K context, in a form small enough for 8 GB consumer GPUs or edge inference.

Prerequisites

vLLM version: >= 0.17.0
Hardware: single 8 GB GPU or Intel Arc Pro B60/B70

Install vLLM

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

Docker

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

Launching the Server

vllm serve Qwen/Qwen3.5-2B \
  --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.

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 \
  --entrypoint bash vllm/vllm-openai-xpu:latest \
  -c "source /opt/intel/oneapi/setvars.sh && exec vllm serve Qwen/Qwen3.5-2B \
  --reasoning-parser qwen3 \
  --enforce-eager"

Client Usage

from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
resp = client.chat.completions.create(
    model="Qwen/Qwen3.5-2B",
    messages=[{"role": "user", "content": "Hi!"}],
    max_tokens=64,
)
print(resp.choices[0].message.content)