DeepSeek Engram: Scaling Large Language Models via Conditional Memory Lookup
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DeepSeek Engram: Scaling Large Language Models via Conditional Memory Lookup
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On January 12, 2026 DeepSeek released its paper on **Engram**, a novel AI architecture that incorporates **conditional memory** to optimize how large language models handle information. By utilizing a **lookup mechanism for static patterns**, this technology separates an AI's logical reasoning from its factual knowledge base. This structural shift allows massive models to run on **cheaper hardware** by offloading memory requirements to standard host RAM without sacrificing speed. Research indicates that this approach effectively **increases model depth**, freeing up the system's core processing power for more complex reasoning and long-context tasks. Ultimately, the **Engram** module enables superior performance across coding, math, and general logic compared to traditional architectures. This innovation suggests a future where AI is significantly **more efficient and accessible** through the strategic decoupling of memory and computation.
Source:
https://github.com/deepseek-ai/Engram/blob/main/Engram_paper.pdf