vLLM
An inference engine optimized for fast LLM serving — uses PagedAttention to avoid wasteful memory fragmentation.
Think of it like
A smart warehouse manager who packs boxes efficiently so none of the shelf space is wasted.
Example
vLLM can serve more concurrent requests per GPU than vanilla PyTorch because it doesn't allocate a full context window per request upfront.
How it actually works
vLLM's key trick is PagedAttention — KV cache (attention keys and values) are paged into blocks dynamically rather than pre-allocated in full. This lets you fit more requests on the same GPU memory. It also supports continuous batching (processing requests at different paces) and speculative decoding.
For product teams
vLLM can cut your inference costs by 3-4x by letting you pack more requests per GPU.
For engineers
vLLM abstracts away low-level CUDA — use its Python API or OpenAI-compatible REST endpoint.
Read anything AI without the jargon
Look up any term in plain English, or save terms as you read with the free Chrome extension.
Open DecoderAdd to Chrome