PagedAttention

Unlocking LLM Performance Through Smarter Memory Management

KV cache memory can quietly decide whether an LLM deployment thrives or stalls. PagedAttention tackles fragmentation with smarter allocation, while RadixAttention targets prefix reuse. Both cut the GPU strain that limits concurrency and throughput. What stands out is how practical these optimizations feel. Production latency improves without demanding new hardware. For readers exploring how context shapes model behavior, our piece on agentic context learning pairs well with this discussion. It is a useful next step for understanding what makes modern LLMs efficient beyond raw architecture.