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LLMFreeOpen Source
VLLM
High-throughput LLM inference and serving engine
Apache-2.0
ABOUT
LLM inference is memory-bound and inefficient due to poor key-value cache management. vLLM solves this with PagedAttention for near-zero waste KV-cache memory, continuous batching, chunked prefill, and prefix caching — delivering state-of-the-art throughput while reducing the GPU hardware needed for production serving.
INSTALL
pip install vllmINTEGRATION GUIDE
1. Deploy high-throughput OpenAI-compatible API servers for production chat and completions
2. Run offline batch inference over large datasets efficiently without maintaining a server
3. Serve massive models across multiple GPUs using tensor, pipeline, and expert parallelism
4. Optimize cloud inference costs by maximizing throughput per GPU for SaaS applications
TAGS
llminferenceservingopen-sourcegpuproduction