vLLM
High-throughput and memory-efficient LLM serving and inference engine.
Overview
vLLM is an open-source library for fast LLM inference and serving. Featuring PagedAttention memory management, continuous batching, and tensor parallelism, it delivers up to 24x higher throughput than Hugging Face Transformers.
Key Capabilities
- PagedAttention algorithm managing KV cache with near-zero memory waste
- Continuous batching of incoming requests for high GPU utilization
- OpenAI-compatible HTTP server interface for drop-in client compatibility
Ideal Use Cases & Goals
- “Serving self-hosted Llama and Mistral models in enterprise production”
- “Maximizing GPU token generation throughput in high-concurrency apps”
- “Replacing expensive proprietary LLM APIs with self-managed open models”
Direct Alternatives
Tools that offer comparable features or target the same creative workflow: