Optimizing LLM Inference With Mooncake: KVCache-Centric Architecture
Mooncake optimizes LLM inference performance with KVCache, SnapKV, PyramidInfer & MiniCache techniques, reducing memory usage & increasing throughput by up to 3x.
This is a Plain English Papers summary of a research paper called Mooncake: Kimi's KVCache-centric Architecture for LLM Serving. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview Mooncake is a novel KVCache-centric architecture for serving large language models (LLMs) efficiently. The paper introduces key techniques like KVCache, SnapKV, PyramidInfer, and MiniCache to optimize LLM inference performance. The architecture also leverages KV Runahead to enable scalable causal LLM inference. Plain English Ex...