Transformers have taken the AI world by storm, especially as the backbone of LLMs. In the paper Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference, the authors introduce a novel solution to the generation inefficiencies inherent in traditional LLMs.

• LLMs typically require storing a growing key-value cache during inference, slowing down the process.
• Dynamic Memory Compression (DMC) enables real-time key-value cache compression.
• The method allows different compression rates across various heads and layers, leading to more intelligent resource utilization.
• Retrofitting pre-trained LLMs with DMC can result in up to ~3.7x increased throughput on a NVIDIA H100 GPU.

This approach retains the original model’s performance even with a 4x cache compression and happens without adding extra model parameters. The DMC method could be a game-changer in making LLMs more accessible and efficient for real-world applications, offering a great promise for scenarios where fast response times are crucial.