Motivation behind KVBM

Large language models (LLMs) and other AI workloads increasingly rely on KV caches that extend beyond GPU and local CPU memory into remote storage tiers. However, efficiently managing the lifecycle of KV blocks in remote storage presents challenges:

• Tailored for GenAI use-cases

• Lack of visibility into real-time block usage patterns.

• Need for lightweight, ownership-driven memory management over complex object stores with unneeded overheads.

• Modular and need simplified UX and to be memory safe.

• Inability to differentiate between hot (frequently accessed) and cold (infrequently accessed) blocks across the stack without intrusive application-level changes.

• Difficulty in optimizing storage placement across heterogeneous storage tiers (for example, SSDs, object storage, and cloud storage).

Conventional systems either lack dynamic feedback mechanisms or require deep integration into core storage paths, which both increases complexity and reduces portability.