KV Cache Isn’t a Caching Problem

Allen Helton argues that the current industry focus on tiered KV cache storage for AI/ML applications, such as those involving large language models (LLMs), is misdirected because it overlooks the unique characteristics of these workloads compared to traditional caching systems. Traditional caching is optimized for high transaction rates with small objects, but LLMs often require handling massive gigabyte-sized objects, making network throughput rather than storage tier the primary bottleneck. As a result, optimizing for time to last byte (TTLB) and intelligent prefetching becomes crucial for GPU utilization, as it reduces idle times by having necessary data ready before the GPU is free. The article suggests that while storage tiering is a visible and tractable issue, it is not the ultimate solution, and the real challenge lies in predicting and preloading the needed context efficiently to enhance GPU performance, a problem that Helton's team at Momento aims to address.