Memorize Step by Step: Efficient Long-Context Prefilling with Incremental Memory and Decremental Chunk
Abstract: The evolution of Large Language Models (LLMs) has led to significant advancements, with models like Claude and Gemini capable of processing contexts up to 1 million tokens. However, efficiently handling long sequences remains challenging, particularly during the prefilling stage when input lengths exceed GPU memory capacity. Traditional methods often segment sequence into chunks and compress them iteratively with fixed-size memory. However, our empirical analysis shows that the fixed-size memory results in wasted computational and GPU memory resources. Therefore, we introduces Incremental Memory (IM), a method that starts with a small memory size and gradually increases it, optimizing computational efficiency. Additionally, we propose Decremental Chunk based on Incremental Memory (IMDC), which reduces chunk size while increasing memory size, ensuring stable and lower GPU memory usage. Our experiments demonstrate that IMDC is consistently faster (1.45x) and reduces GPU memory consumption by 23.3\% compared to fixed-size memory, achieving comparable performance on the LongBench Benchmark.
Paper Type: Long
Research Area: Special Theme (conference specific)
Research Area Keywords: Special Theme, Language Modeling
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 4637
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