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How to Deploy Qwen3.5-27B with vLLM

Younes El Hjouji

Detailed Deployment Instructions

1. Environment Setup

# Create a working directory (choose your preferred location)

# Create virtual environment with Python 3.12
python3.12 -m venv venv

# Activate virtual environment
source venv/bin/activate

# Verify Python version (should be 3.12.x)
python --version

# Upgrade pip
pip install --upgrade pip

# Install vLLM nightly (REQUIRED — Qwen3.5 is not yet in stable vLLM)
# Option A: Using pip with nightly index
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

# Option B: If nightly wheels are unavailable for your platform,
# reuse a venv from another Qwen3.5 model that already has nightly installed

# Install video support
pip install 'vllm[video]'

2. Launch Deployment

# Make sure you're in your working directory and activate virtual environment
source venv/bin/activate

# Launch vLLM serve
vllm serve Qwen/Qwen3.5-27B \
  --dtype bfloat16 \
  --max-model-len 32768 \
  --max-num-batched-tokens 32768 \
  --gpu-memory-utilization 0.90 \
  --reasoning-parser qwen3 \
  --default-chat-template-kwargs '{"enable_thinking": false}' \
  --port 8000

Note: First model download takes ~5-10 minutes. First inference request triggers CUDA graph compilation which may take several minutes. Subsequent requests are fast.

3. Verification

3.1. Check server is running:

curl http://localhost:8000/v1/models

3.2. Test text inference:

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.5-27B",
    "prompt": "Hello, how are you?",
    "max_tokens": 50
  }'

3.3. Test video inference:

VIDEO_BASE64=$(base64 -w 0 /path/to/your/video.mp4)

time curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"Qwen/Qwen3.5-27B\",
    \"messages\": [{
      \"role\": \"user\",
      \"content\": [
        {
          \"type\": \"video_url\",
          \"video_url\": {
            \"url\": \"data:video/mp4;base64,\$VIDEO_BASE64\"
          }
        },
        {
          \"type\": \"text\",
          \"text\": \"Describe the user actions in this video in a sequential list.\"
        }
      ]
    }],
    \"max_tokens\": 1024
  }"

3.4. Test with thinking enabled (per-request):

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.5-27B",
    "messages": [{"role": "user", "content": "What is 27 * 13?"}],
    "max_tokens": 512,
    "chat_template_kwargs": {"enable_thinking": true}
  }'

Verified Results:

  • Text inference: Working. Response in <0.01s (warm).
  • Video inference: Working. video.mp4 (6s clip) processed in ~0.9s.
  • Video output correctly identified soccer player in red jersey, shouting/gesturing on grassy field.
  • Thinking disabled by default, can be toggled per-request.

4. Troubleshooting

Issue 1: First request takes several minutes

  • Solution: This is expected. CUDA graph compilation happens on the first request. Subsequent requests are fast.

Issue 2: Requires vLLM nightly

  • Solution: Qwen3.5 architecture (qwen3_5) is only supported in vLLM nightly builds. Install from https://wheels.vllm.ai/nightly. If nightly wheels are unavailable for your platform, check for a compatible recent nightly or reuse an existing Qwen3.5 venv.

Issue 3: Thinking tokens appear in content

  • Solution: Ensure --reasoning-parser qwen3 is set on the server. To disable thinking entirely, add --default-chat-template-kwargs '{"enable_thinking": false}'.

5. Notes

  • This is the only dense model in the Qwen3.5 family — all 27B parameters are active per token. All other Qwen3.5 variants use MoE.
  • GPU memory for weights (~51GB) is lower than the 35B-A3B (~70GB) despite similar effective quality, because there are no inactive MoE experts to store.
  • The model uses a novel hybrid attention mechanism (Gated DeltaNet + Gated Attention) which provides faster inference on long contexts compared to standard Transformer attention.
  • The model naturally produces <think> reasoning traces when thinking is enabled. Use --default-chat-template-kwargs '{"enable_thinking": false}' to disable by default.
  • Chunked prefill is automatically enabled with max_num_batched_tokens=32768.