serving-llms-vllm

Serve LLMs with high throughput using vLLM by leveraging PagedAttention and continuous batching to maximize GPU utilization. It provides OpenAI-compatible endpoints for scalable API deployment and batch inference workloads.

You can deploy LLMs on memory-constrained GPU clusters using vLLM with quantization options like AWQ, GPTQ, and FP8, alongside tensor parallelism to distribute memory load across multiple GPUs.

vLLM provides an OpenAI-compatible API endpoint for input and output routing. This allows easy integration of your served LLMs with existing tooling built for OpenAI API standards.

Maximize GPU utilization during LLM inference by using vLLM's PagedAttention and continuous batching mechanisms. This approach optimizes memory allocation and request handling for production-grade workloads.

Tensor parallelism for LLM serving requires multiple GPUs to distribute model weights effectively. While it enables memory-constrained deployments, scaling across clusters depends on available hardware and interconnect bandwidth.