AI Summary: This MIT-led research solves the 'memory wall' problem for LLMs by drastically shrinking the amount of data the model needs to store in its active memory. Attention Matching identifies the most important parts of a conversation or document and 'compacts' the rest without losing the model's ability to recall key details.
Large Language Models struggle with memory overhead during long-context inference due to the linear growth of the Key-Value (KV) cache. We propose Attention Matching (AM), a framework for high-quality KV cache compaction that maintains performance while achieving 50x compression ratios. Unlike prior latent-space methods, AM uses a differentiable matching objective to ensure the compressed cache retains the most task-relevant information. Our method is two orders of magnitude faster than existing compaction techniques, enabling real-time long-context processing on consumer-grade hardware.
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