The Evolution of Memory Architecture for KV Cache Management
AccBalanced · x · 2026-07-17
This repost focuses on KV cache management in LLM inference. The core viewpoint is that this might be one of the hardest problems in inference systems, and the community needs to learn from each other and continuously iterate.
The cited content details a refactor by TokenSpeed: they changed their memory pool, originally based on a Radix Tree, into a flat, block-based KV cache architecture. The new solution uses a single flat paged pool and provides heterogeneous views, aiming to make it easier to support different attention mechanisms. The text also mentions that similar approaches are being explored in the community, such as vLLM's Jenga and LMDeploy's TurboMind. This refactor occurred around the release of TML's Inkling, allowing the new architecture to support Inkling from day one.
Related event: Evolution and Refactoring of KV Cache Architecture in LLM Inference(2 posts)→
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