The explosion of open-source large language models like Llama 3, Mistral, and Qwen has made self-hosted AI inference accessible to organizations of all sizes. Running these models on Linux servers with vLLM provides production-grade inference performance with PagedAttention for efficient memory management.
Setting Up a vLLM Inference Server
vLLM is a high-throughput LLM serving engine that leverages PagedAttention to manage GPU memory more efficiently than naive KV-cache implementations. On a Linux server with an NVIDIA A100 or H100 GPU, vLLM can serve Llama 3 70B at hundreds of tokens per second with continuous batching enabled.
Installation on Ubuntu or RHEL-based distributions is straightforward with pip, though ensuring the correct CUDA toolkit version and driver compatibility is essential. Docker containers with NVIDIA Container Toolkit provide the most reproducible deployment, isolating dependencies and simplifying upgrades.
For production deployments, combining vLLM with a reverse proxy like NGINX, Prometheus metrics collection, and Grafana dashboards creates a robust serving stack. Model quantization with GPTQ or AWQ reduces memory requirements, allowing larger models to fit on fewer GPUs without significant quality degradation.