Shypd.aiThe largest open index of AI toolsH2O is an open-source AI tool that optimizes generative inference for large language models. It significantly reduces memory footprint and improves throughput by focusing on 'Heavy Hitter' tokens.
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H2O (Heavy-Hitter Oracle) is an open-source project designed to enhance the efficiency of generative inference in Large Language Models (LLMs). It addresses the challenge of high memory consumption, particularly with the KV cache, which scales with sequence length and batch size. H2O introduces a novel KV cache eviction policy that identifies and retains 'Heavy Hitter' tokens—those contributing most to attention scores—while dynamically balancing them with recent tokens. This approach is based on the observation that a small portion of tokens are critical for performance. The implementation of H2O has demonstrated significant improvements, boosting throughput by up to 29x over leading inference systems like DeepSpeed Zero-Inference, Hugging Face Accelerate, and FlexGen, and reducing latency by up to 1.9x on models like OPT-6.7B and OPT-30B.
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