[R] Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Paper: [https://arxiv.org/abs/2403.09636](https://arxiv.org/abs/2403.09636)

X: [https://x.com/p\_nawrot/status/1768645461689168365](https://x.com/p_nawrot/status/1768645461689168365)

Abstract:

>Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the input sequence length and batch size. As a solution, we propose Dynamic Memory Compression (DMC), a method for on-line key-value cache compression at inference time. Most importantly, the model learns …

abstract cache compression dynamic however inference key language language models large language large language models llms machinelearning memory store tokens transformers value