InfiniteHiP: Efficient Long-Context LLM Inference

Substantial speedups and improved performance on long-context tasks without requiring additional training or model modifications

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1. Short summary

2. How this advances on prior methods

3. Analogy

4. What does this mean for the future

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InfiniteHiP: Efficient Long-Context LLM Inference

Authors:

• Heejun Lee

• Geon Park

• Jaduk Suh

• Sung Ju Hwang

Publication date: February 12, 2025

Paper link: https://arxiv.org/pdf/2502.08910

Short Summary

The efficient processing of extended context lengths in large language models (LLMs) poses a significant computational challenge, impacting both inference speed and memory consumption.

Existing pre-trained LLMs often exhibit limited generalization capabilities beyond their training sequence lengths. To address these limitations, the author’s present InfiniteHiP, a novel and practical inference framework engineered for efficient long-context utilization. InfiniteHiP introduces a modular, hierarchical token pruning algorithm that dynamically eliminates irrelevant context tokens, thereby accelerating processing.

Critically, it facilitates generalization to extended sequence lengths by selectively applying diverse RoPE adjustment methodologies, informed by the internal attention patterns of the LLM. Additionally, the framework incorporates key-value cache offloading to host memory, substantially alleviating GPU memory pressure.

Stemming from these stacked improvements InfiniteHiP enables the processing of up to 3 million tokens on a single L40s 48GB GPU—a 3x increase—without compromising context integrity. Empirical evaluation demonstrates an 18.95x speedup in attention decoding for a 1 million token context, achieved without requiring additional training. Implementation within the SGLang framework underscores the effectiveness and practical applicability of InfiniteHiP.

How This Advances On Prior Methods