KV cache tiering: Why GPU memory alone won't scale your LLM app

Large language models (LLMs) face significant challenges in scaling for production due to the growing demand on key-value (KV) caches, which store intermediate representations for reuse in future inferences. As context windows expand and multi-turn interactions become more common, the KV cache demands more GPU memory, which is limited and becomes a bottleneck. Managing this cache efficiently is crucial as each token processed generates key and value vectors that need to be stored for future reference, leading to significant memory use. Tools like LMCache address this by storing and reusing cached data, thereby reducing inference latency and computational resources. However, GPU memory constraints lead to issues like cache eviction, cross-worker cache misses, and restart penalties, necessitating a multi-tiered storage approach to maintain performance and scalability. This involves using various storage layers beneath GPU memory, such as CPU RAM, SSDs, and remote KV stores, to manage cache data more effectively. This tiered architecture enables high-throughput bulk transfers, asynchronous writes, and policy-driven eviction, essential for handling the large tensor objects characteristic of AI inference workloads.

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