Deepseek research touts memory breakthrough, decoupling compute power and RAM pools to bypass GPU & HBM constraints — Engram conditional memory module commits static knowledge to system RAM

Deepseek has introduced a new memory breakthrough called Engram, which separates compute power and memory pools to enhance AI model performance by storing static knowledge in system RAM. This method aims to reduce reliance on high-bandwidth memory (HBM) and improve long-context query performance by committing data sequences to static memory. Engram allows AI models to remember facts instead of reasoning them out, leading to more efficient GPU utilization. The paper suggests that Engram could revolutionize AI models by improving performance in various tasks and potentially reducing the industry's reliance on HBM. While the impact of Engram in real-world deployment remains to be seen, it has the potential to significantly enhance AI models and reshape memory usage in data centers.