DeepSeek Engram hits 97% on NIAH using DRAM instead of HBM (www.techaffiliate.in)

DeepSeek has announced its groundbreaking research on a new memory architecture called Engram, which aims to tackle the high costs associated with AI infrastructure. By integrating a conditional memory system that utilizes cheaper DRAM instead of the traditionally favored High Bandwidth Memory (HBM), Engram allows AI models to store static knowledge and perform computations more efficiently. This shift significantly reduces extensive GPU compute requirements for recalling basic facts, leading to dramatic cost savings and increased performance—DeepSeek's tests showed an impressive 97% accuracy in long-context tasks compared to just 84.2% for traditional models. This development is critical for the AI/ML community as it addresses the ongoing memory bottleneck that threatens to hinder AI scalability. Engram’s architecture allows for faster, cheaper inference by enabling O(1) memory lookups for frequently asked queries, freeing up GPU resources for complex reasoning tasks. This can lead to lower deployment costs, faster rollouts in varied hardware environments, and reduced reliance on expensive memory resources. If successful in production, particularly with larger models, Engram could redefine AI economics and deployment strategies, presenting a significant competitive advantage for those who can swiftly adapt to this new paradigm of efficient memory utilization.