vLLM
Throughput, memory and concurrency are the three things vLLM cares about. There is no chat interface; it turns open models into an API that holds up under production traffic, while tool calling, MoE backends and long-context caching keep improving.
vLLM sits between the model and the application: front ends and knowledge bases are not its job, it turns open models into stable, memory-efficient services that hold up under concurrency. Teams hosting their own model API shortlist it; anyone who only wants local chat finds it too heavy. Recent releases fix the substrate: fused MoE for TRTLLM, FP8 backends and MTP. Tool calling needs --enable-auto-tool-choice and the right parser.
