KV-Cache Management

The field studies how to store, reuse, compress, and move the key-value states that Transformers accumulate during generation, which can dominate memory and data-transfer costs in long-context inference. Methods include token eviction and learned importance scoring, quantization and compact representations, prefix or cross-request sharing, and offloading across GPU, host memory, and storage; serving systems also coordinate these choices with scheduling and batching. Evaluations commonly measure quality on long-context retrieval and reasoning benchmarks such as LongBench and RULER alongside memory use, time to first token, and throughput. The central tension is reducing cache cost without losing information needed later: results depend on the task, workload, hardware, and attention implementation, so robust policies and faithful end-to-end evaluation remain important challenges.