serving-llms-vllm

Serve LLMs with an OpenAI-compatible API by deploying vLLM, which exposes a `/v1`-style interface allowing you to reuse OpenAI SDK-style clients for high-throughput text generation. You can quickly start a server using the `vllm serve` command with your chosen model.

vLLM achieves high-throughput LLM serving through PagedAttention, a block-based KV cache mechanism, combined with continuous batching. This architecture significantly increases tokens per second for multi-user workloads while maintaining low latency.

You can fit larger LLMs on limited GPUs using vLLM by applying tensor parallelism alongside quantization modes like AWQ, GPTQ, or FP8. This memory-efficient scaling approach reduces the overall VRAM footprint required for deployment.

Monitor and troubleshoot vLLM production deployments by utilizing Prometheus for metrics tracking and Docker for containerized deployment. This setup provides guidance for resolving OOM, TTFT, and throughput issues during high-throughput inference operations.

vLLM supports offline batch inference for text generation in addition to production API serving. It leverages PagedAttention and continuous batching to efficiently process large volumes of offline generation requests with high throughput.