#vllm

vLLM serves 10x more requests than naive PyTorch. PagedAttention, continuous batching, and memory management make the difference.

vLLM, SGLang, TensorRT-LLM—each optimizes for different things. Here's how to pick without running a 6-month bake-off.

vLLM doesn't use a faster model. It uses memory smarter. PagedAttention treats KV cache like virtual memory, and the results are dramatic.

Static batching wastes GPU cycles waiting for the slowest request. Continuous batching fills those gaps. The difference is 3-5x throughput.