serving-llms-vllm

Deploy an LLM for high-throughput inference by configuring vLLM with PagedAttention and continuous batching. This approach enables production-grade serving with low latency, utilizing OpenAI-compatible endpoints to handle large-scale inference workloads efficiently across compatible GPU clusters.

Fit large models on limited GPU memory during LLM serving by applying quantization options and tensor parallelism. vLLM supports AWQ, GPTQ, and FP8 quantization alongside tensor parallelism to distribute model weights and reduce the overall memory footprint effectively.

Run vLLM in a Kubernetes environment by following deployment guidance for Docker and Kubernetes configurations. This setup includes configuring containerized vLLM instances with optional metrics monitoring to ensure stable production workloads and simplified troubleshooting across the cluster.

You need a CUDA-enabled GPU cluster to use vLLM for serving because the framework relies on GPU acceleration for PagedAttention and continuous batching. Additional requirements include a compatible Python environment, the vllm package, and access to target models.

Continuous batching improves LLM inference performance by dynamically processing incoming requests without waiting for a batch to complete. Combined with PagedAttention in vLLM, it maximizes GPU utilization and significantly increases throughput for concurrent OpenAI-compatible endpoint calls.

vLLM provides OpenAI-compatible endpoints for production deployment, allowing seamless integration with existing applications expecting standard API formats. This ensures that deployed models with quantization and tensor parallelism are accessible via familiar inference interfaces.