IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs

IceCache is a new memory management technique for large language models that reduces KV cache memory consumption by 75% while maintaining 99% accuracy on long-sequence tasks. The method combines semantic token clustering with PagedAttention to intelligently offload cache data between GPU and CPU, addressing a critical bottleneck in LLM inference on resource-constrained hardware.

IceCache addresses a fundamental computational challenge in modern LLM deployment: the memory wall created by Key-Value caching during inference. As language models generate longer sequences, KV cache memory requirements grow linearly, making inference prohibitively expensive on hardware with limited VRAM. This research demonstrates a practical solution that maintains model quality while dramatically reducing memory footprint—a critical requirement for democratizing LLM access across diverse hardware platforms.

The innovation lies in moving beyond naive token selection strategies by applying semantic clustering to group contextually related tokens into contiguous memory blocks. This enables more intelligent decisions about which tokens remain in high-speed GPU memory versus slower CPU storage. By organizing these clusters within a dynamically updatable hierarchical data structure, IceCache optimizes memory bandwidth utilization during CPU-GPU transfers, reducing the performance penalties typically associated with offloading approaches.

For the AI infrastructure market, this development has significant implications. Currently, inference costs represent a major operational expense for AI service providers, particularly for long-context applications like document analysis and chain-of-thought reasoning. IceCache enables deployment on commodity hardware previously unsuitable for such workloads, potentially shifting economics for edge computing and on-device inference. The ability to achieve competitive performance with 25% of previous token budgets suggests meaningful cost reductions in production systems.

The technique particularly impacts emerging applications requiring extended context windows. As models like GPT-4 and Claude expand their context capabilities, memory-efficient inference becomes increasingly valuable. Future developments may integrate IceCache principles into production inference engines, fundamentally reshaping hardware requirements and competitive dynamics in the AI infrastructure space.

• →IceCache reduces KV cache memory usage by 75% while maintaining 99% accuracy on long-sequence benchmarks
• →Semantic token clustering enables more intelligent CPU-GPU memory offloading decisions than prior approaches
• →The method shows competitive or superior performance compared to existing offloading solutions at significantly lower memory budgets
• →Long-context inference applications like chain-of-thought reasoning benefit substantially from reduced memory bottlenecks
• →Technique enables cost-effective LLM deployment on resource-constrained hardware previously unsuitable for production inference