What It Does
vLLM is a high-throughput inference engine for large language models that achieves near-optimal GPU memory utilization through PagedAttention. It dramatically increases serving throughput compared to naive implementations by managing KV-cache memory like virtual memory pages.
Key Features
vLLM provides PagedAttention for efficient memory management, continuous batching for high throughput, tensor parallelism for multi-GPU serving, OpenAI-compatible API server, support for 50+ model architectures, speculative decoding, and quantization support.
When To Use It
Use vLLM when you need to serve LLMs at production scale with maximum throughput. It is ideal for API services, batch processing, and any scenario where GPU utilization and requests-per-second matter. Supports most popular open models.
Alternatives
TGI offers similar performance with Hugging Face ecosystem integration. Triton Inference Server provides enterprise features and multi-framework support. Ollama targets local development with a simpler interface.