meta:
  title: "Qwen3.5-0.8B"
  slug: "qwen3.5-0.8b"
  provider: "Qwen"
  description: "Qwen3.5 tiny dense multimodal model (0.8B) — ultra-low-VRAM / edge serving with 262K context"
  date_added: 2026-04-22
  date_updated: 2026-09-14
  tasks:
    - multimodal
    - text
  performance_headline: "Tiny Qwen3.5 dense for edge / draft-model use"
  related_recipes:
    - "Qwen/Qwen3.5-2B"
  hardware:
    arc_pro_b60: verified
    arc_pro_b70: verified

model:
  model_id: "Qwen/Qwen3.5-0.8B"
  min_vllm_version: "0.17.0"
  default_frontend: rust
  architecture: dense
  parameter_count: "0.8B"
  active_parameters: "0.8B"
  context_length: 262144
  base_args:
    - "--trust-remote-code"
  base_env: {}

features:
  tool_calling:
    description: "Enable automatic tool choice with Qwen3 Coder parser"
    args:
      - "--enable-auto-tool-choice"
      - "--tool-call-parser"
      - "qwen3_coder"
  reasoning:
    description: "Enable chain-of-thought reasoning with Qwen3 parser"
    args:
      - "--reasoning-parser"
      - "qwen3"
  text_only:
    description: "Skip loading the vision encoder for text-only workloads."
    args:
      - "--language-model-only"

opt_in_features:
  - text_only

variants:
  default:
    precision: bf16
    vram_minimum_gb: 2
    description: "Full precision BF16 — runs on any modern GPU"

compatible_strategies:
  - single_node_tp

hardware_overrides:
  xpu:
    extra_args:
      - "--enforce-eager"

strategy_overrides: {}

guide: |
  ## 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)