arXiv:2510.07651v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all key-value (KV) states scales linearly with sequence length and batch size. Existing cache eviction methods address this by exploiting attention sparsity, yet they typically rank tokens heuristically using accumulated attention weights without considering their true impact on attention outputs. We propose Optimal Brain Cache (OBCache), a principled framework that formulates cache eviction as a lay
The rapid expansion of LLM context windows has made efficient memory management for Key-Value caches a critical bottleneck, driving active research into optimization techniques.
Efficient long-context LLM inference reduces operational costs and expands the practical applications of AI, impacting the economic viability and capabilities of AI systems.
This research provides a more principled method for LLM cache eviction, potentially leading to more efficient and scalable deployment of powerful, long-context AI models.
- · Large Language Model developers
- · Cloud providers
- · AI-powered application developers
- · Data center operators
- · Less efficient AI inferencing methods
- · Companies with high LLM operational costs
More cost-effective and performant LLMs capable of handling much longer contexts become widely available.
This efficiency gain enables a broader range of complex AI applications, particularly those requiring extensive historical data or conversational memory.
Reduced compute requirements could somewhat alleviate pressures on critical resources like HBM and energy, indirectly benefiting the broader compute supply chain.
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Read at arXiv cs.AI