KV Caching Pays Off Under Load

KV caching, while essential during inference to prevent models from recomputing attention over the entire context for each token, presents challenges when considered as a long-term solution due to its high memory demands, complexity, and limited reuse model. Although single-request latency improvements appear modest, especially when evaluated in isolation, the real value of KV caching emerges in high-concurrency environments where it can significantly reduce system-level latency by minimizing redundant prefill work. Recent advancements in attention mechanisms and caching techniques, such as Multi-head Latent Attention and methods like TurboQuant, have drastically reduced the memory footprint of KV caches, making them more economically viable. As inference systems increasingly adopt distributed architectures, where prefill and decode processes are disaggregated, the role of KV caching evolves from being a mere optimization tool to a critical systems primitive that enhances overall throughput and efficiency. The potential of KV caching is further underscored by the ongoing exploration into cache repair techniques, which aim to improve cache hit rates by making cached entries more adaptable across different requests.