arXiv:2602.07721v3 Announce Type: replace Abstract: KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scale. We introduce ParisKV, a drift-robust, GPU-native KV-cache retrieval framework based on collision-based candidate selection, followed by a quantized inner-product reranking estimator. For million-token contexts, ParisKV supports CPU-offloaded KV caches via Unified Virtual Addressing (UVA), enabling on-demand top-$k$ fetching with minimal overhead. ParisKV matches or outperforms full attention quality
Advances in LLM architecture and the compute demands of long-context windows necessitate more efficient KV-cache management. This type of research is emerging as the scaling limits of current methods are being reached. It reflects ongoing innovation within the AI compute optimization space.
Efficient and robust KV-cache retrieval is critical for scaling long-context LLMs, directly impacting their performance, cost, and overall utility. Innovations in this area unlock new capabilities for AI agents and complex analytical tasks, pushing the frontier of practical LLM deployment.
The ability to manage trillion-token contexts more effectively with lower latency and higher quality akin to full attention drastically expands the operational scope and economic viability of extremely large context windows in LLMs. It directly addresses a major bottleneck in scaling LLMs.
- · LLM developers
- · Cloud providers
- · AI-powered applications
- · Users requiring long-context analysis
- · Inefficient KV-cache solutions
- · Companies unable to adopt advanced LLM inference techniques
Longer-context LLMs become more practical and affordable for real-world applications.
New categories of AI agents and complex data analysis tools emerge, leveraging previously unmanageable context lengths.
The demand for specialized hardware and optimized software for LLM inference continues to accelerate compute supply chain innovation.
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Read at arXiv cs.LG