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A detailed blog post explaining how vLLM works, including PagedAttention, KV cache management, and continuous batching for efficient LLM serving.
Explains why LLM inference is increasingly memory-bandwidth bound due to the KV cache scaling with context length and concurrent users, and how systems like vLLM and PagedAttention improve memory utilization.
vLLM 0.20.0rc1 releases with major throughput, quantization, speculative decoding, and multi-hardware support enhancements for scalable LLM serving.