Efficient Memory Management for Large Language Model Serving with PagedAttention

The reason an inference server can hold many concurrent requests: treating the KV cache like virtual memory pages instead of one contiguous block removes the fragmentation that was wasting most of the GPU. It is the paper behind vLLM, which is what most self-hosted serving actually runs.

• KV cache — The memory a model keeps about text it has already read, so generating each new token doesn't require reprocessing the whole conversation.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

• postInside the vLLM Changes That Multiplied Agentic Inference ThroughputSemiAnalysis_
• articleRecent Developments in LLM Architectures: KV Sharing, mHC, and Compressed AttentionSebastian Raschka, PhD
• articleUnderstanding and Coding the KV Cache in LLMs from ScratchSebastian Raschka, PhD

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