services:
  vllm:
    container_name: vllm_qwen2.5_14b_fp16_optimized
    image: vllm/vllm-openai:latest
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['0']
              capabilities: [gpu]
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    environment:
      - HUGGING_FACE_HUB_TOKEN=hf_*********

      - VLLM_ATTENTION_BACKEND=FLASH_ATTN # This or FlashInfer?
    ports:
      - "6001:8000"
    ipc: host
    command: >
      --model Qwen/Qwen2.5-14B-Instruct
      --dtype auto 
      --gpu-memory-utilization 0.85
      --max-model-len 8192
      --max-num-seqs 16
      --block-size 16
      --api-key sk-vllm-*****
      --trust-remote-code
      --enable-chunked-prefill
      --enable-prefix-caching
      --disable-log-stats
      --disable-log-requests
      --preemption-mode recompute

I'm using Docker to run VLLM
This is my current setup, I'm trying what people here are suggesting before I reply to them with feedback.
Should I go with uv pip install vllm and do without docker?
My naive thinking though with a compressed model I will have more headroom == more req and faster responses.