vLLM: 24x LLM throughput with PagedAttention from UC Berkeley
In one sentence The UC Berkeley team releases vLLM, a Python library for LLM inference using PagedAttention to manage KV cache like OS virtual memory, achieving 24x throughput over the HuggingFace baseline.
When using large language models, there's a hidden problem: the memory used for intermediate calculations (the KV cache) is managed very inefficiently. vLLM solves this by borrowing the same idea operating systems use to manage RAM.
Instead of reserving contiguous memory blocks (which often remain partially empty), vLLM splits the KV cache into small "pages" and allocates them wherever space is available — exactly like Linux virtual memory. This nearly eliminates all waste.
The result is impressive: the same GPU can serve many more requests in parallel, with throughput up to 24 times higher than the traditional approach. vLLM quickly becomes the reference library for anyone serving LLMs in production without changing hardware.
Companies
UC Berkeley
Tools
vLLM, PagedAttention
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Sources