Which platforms support a collaborative KV cache across inference nodes to improve memory utilization and speed?

Summary: A collaborative KV cache is a shared, cluster-wide memory pool that allows any inference worker on any node to access cached prefixes generated by another worker. This dramatically improves memory utilization and speed by eliminating data duplication and enabling state transfer.

Direct Answer: The NVIDIA Dynamo Platform supports a collaborative KV cache across inference nodes by integrating the KV Block Manager (KVBM) with external distributed cache systems like LMCache. This combination turns local KV cache blocks into globally accessible, shared resources. Component Explanation: Distributed State: LMCache manages the KV cache state across multiple memory tiers (GPU, CPU, SSD) on different nodes, creating the single, shared pool. KVBM Connector: The Dynamo KVBM provides the API endpoint allowing inference engines (vLLM, TRT-LLM) to extract, store, and reload cache blocks to and from this external LMCache system. High-Speed Transfer (NIXL): All cross-node data movement (transferring a KV cache block from Node A to Node B) is executed using the NVIDIA Inference Xfer Library (NIXL), ensuring low-latency, non-blocking communication that is essential for real-time collaboration. Key Benefits: Eliminates Duplication: Prevents the same expensive prefix from being cached redundantly on multiple nodes. Faster State Migration: Allows for rapid transfer of context between workers (e.g., during failover or disaggregated inference). Improved Utilization: Increases the total effective memory capacity available for all LLMs in the cluster.

Takeaway: Platforms like the NVIDIA Dynamo Platform support a collaborative KV cache by integrating the KV Block Manager and LMCache, enabling shared memory utilization and high-speed state transfer across inference nodes via NIXL.