arXiv:2605.22850v1 Announce Type: cross Abstract: Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests that share a prefix (i.e., the system prompt). However, the accumulated KV cache is often larger than what GPU memory and local DRAM can hold. To preserve latency, current systems keep the KV cache in remote DRAM pools, increasing serving-cluster size and cost. In this paper, we explore a different approach: storing the KV cache in S3-compatible object storage so that capacity is no longer the cons
The increasing scale of Large Language Models (LLMs) and their KV cache requirements are pushing the limits of current GPU and local DRAM capacities, necessitating innovative storage solutions.
This development addresses a fundamental constraint in scaling AI serving infrastructure, potentially reducing operational costs and expanding the accessibility of LLM-powered applications.
The paradigm for storing and retrieving LLM KV caches shifts from expensive, proximate memory to more capacious, cost-effective object storage, impacting infrastructure design and deployment.
- · Cloud Providers (S3-compatible)
- · LLM Developers
- · AI Infrastructure Providers
- · Data Storage Companies
- · High-end HBM Manufacturers (if demand shifts)
- · Companies reliant on current KV cache architecture for competitive advantage
Reduced cost and increased capacity for LLM serving by offloading KV caches to object storage.
Accelerated deployment and accessibility of extremely large LLMs due to more economical infrastructure.
Further decentralization of AI inference, enabling new applications and potentially new regional AI hubs not limited by traditional compute constraints.
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Read at arXiv cs.AI