vLLM
High-throughput, memory-efficient LLM inference and serving engine with PagedAttention and continuous batching — built for production GPU workloads.
model-serving-frameworksRecently released
74
Hero Score
Popularity
76
Performance
100
Ecosystem
75
Maturity
61
Dev Experience
57
⭐ 81,542 stars⬇ 1.3M downloads/wkFirst release: Jun 2023Last release: May 2026
Async Support: YesPlugin Extensions: HighSpeed: Very fastDoc Quality: HighLearning Curve: Medium
Pros
- • PagedAttention and continuous batching deliver state-of-the-art throughput on GPU workloads
- • Ships an OpenAI-compatible HTTP server, so existing clients can point at vLLM with no code changes
- • Broad model support including LoRA adapters and multiple quantization formats
Cons
- • GPU and CUDA are effectively required for production deployments
- • Config tuning at scale (batch sizes, KV cache, parallelism) is non-trivial
- • Linux-first — macOS and Windows support is limited