Implementation Blueprint

Prerequisites

# Verify NVIDIA GPU and drivers
nvidia-smi

# Install vLLM via Pip or use Docker
pip install vllm

Production Deployment (vLLM + OpenAI API)

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-13b-chat-hf \
    --tensor-parallel-size 1 \
    --host 0.0.0.0 \
    --port 8080 \
    --gpu-memory-utilization 0.90

Deployment with Docker Compose

version: '3.8'

services:
  llama-server:
    image: vllm/vllm-openai:latest
    ports:
      - "8000:8000"
    environment:
      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
    command: >
      --model meta-llama/Llama-2-13b-chat-hf
      --max-model-len 4096
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Scaling Strategy

• Tensor Parallelism: While a 13B model usually fits on one GPU, you can use --tensor-parallel-size 2 to split the workload across two smaller GPUs for lower latency per token.
• Dynamic Batching: Configure vLLM to handle dozens of concurrent requests by batching them into a single GPU pass.
• Shared Storage: If running multiple nodes, use a shared volume for the model weights to avoid redundant 26GB downloads across the cluster.

Backup & Safety

• Periodic Evaluation: Set up automated benchmarks (using tools like RAGAS) to ensure the model output quality hasn't degraded after updates.
• Quantization Trade-offs: Always test 4-bit vs 8-bit vs FP16 versions to find the right balance between speed and factual accuracy for your specific use case.
• Secure Endpoints: Never expose the raw vLLM/Ollama port to the internet; always use an authenticated gateway or VPN.