vLLM: Faster LLM Inference with PagedAttention
vLLM is a serving engine that speeds up LLM inference by treating GPU memory like virtual memory. It's used to serve models with higher throughput by batching requests without wasting memory on padding.
vLLM is a serving engine that boosts LLM inference throughput by treating GPU memory like virtual memory via its core PagedAttention algorithm. It's crucial for production systems serving many concurrent users, using continuous batching to maximize GPU utilization and prevent memory fragmentation in the KV cache. The key footgun is mistaking vLLM for a model; it's a high-performance serving framework that runs existing models.
Read the original → Wikipedia: VLLM
- #llm
- #inference
- #gpu
- #serving
- #optimization
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