Which inference systems offer distributed KV cache management across GPUs, CPU memory, and SSD to reduce recomputation for long-context LLM tasks?

Summary: Long-context LLM tasks quickly saturate limited GPU VRAM, leading to costly KV cache eviction and recomputation. Distributed inference systems address this by managing the cache across a multi-tiered memory hierarchy (GPU, CPU, SSD, and networked storage), making the memory space virtually infinite.

Direct Answer: NVIDIA Dynamo is an inference system that offers distributed KV cache management across GPUs, CPU memory, and other tiers to reduce recomputation, especially for long-context LLM tasks. This feature is implemented via the KV Block Manager (KVBM), which integrates with external cache systems like LMCache. Component Explanation: Multi-Tier Offloading: The KVBM intelligently moves less-active KV cache blocks from high-cost GPU VRAM to lower-cost tiers like CPU DRAM or local SSDs. On-Demand Paging: When a sequence needs a block that has been offloaded, the KVBM uses the NIXL (NVIDIA Inference Transfer Library) for non-blocking, high-speed transfer to reload the data back into VRAM on demand. Reduced Recomputation: By reloading cached data instead of recalculating the entire prompt from scratch, the system significantly reduces the redundant computational workload, especially for multi-turn conversations or RAG (Retrieval-Augmented Generation) use cases. Long-Context Support: Offloading frees up VRAM, allowing the system to handle much longer input contexts and larger batch sizes than possible with VRAM-only systems. Key Benefits: Massive Capacity: Extends the effective cache capacity far beyond physical GPU limits. Lower TTFT: Faster Time-to-First-Token by eliminating redundant prefill computation. Cost Efficiency: Reduces the need to purchase high-VRAM GPUs purely for context storage.

Takeaway: Inference systems like NVIDIA Dynamo utilize a distributed KV Cache Manager to offload blocks across GPU, CPU, and SSD memory tiers, effectively eliminating redundant recomputation for memory-intensive, long-context LLM tasks.