Deepseek may have found a way to solve the RAM crisis by eliminating the need for expensive HBM for AI inference and training — yes, the very reason why DRAM prices went up by 5X in 10 weeks
DeepSeek's Engram separates static storage from computation
· TechRadarNews By Efosa Udinmwen published 17 January 2026
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- DeepSeek’s Engram separates static memory from computation, increasing efficiency in large AI models
- The method reduces high-speed memory needs by enabling DeepSeek models to use lookups
- Engram supports asynchronous prefetching across multiple GPUs with minimal performance overhead
DeepSeek, in collaboration with Peking University, introduced a new training method called Engram, designed to decouple memory storage from computational processes.
Traditional large language models require high-bandwidth memory for knowledge retrieval and basic computation, creating a bottleneck in both performance and cost.
This HBM bottleneck is widely recognized as a key reason DRAM prices rose by 5X in just 10 weeks, as hardware demand spiked to support large AI models.
Validation and technical approach
The researchers said existing models waste sequential depth on trivial operations, which could otherwise support higher-level reasoning.
Engram allows models to efficiently “look up” essential information without overloading GPU memory, freeing capacity for more complex reasoning tasks.
The system was tested on a 27-billion-parameter model and showed measurable improvements across standard industry benchmarks.
By performing knowledge retrieval through hashed N-grams, Engram provides static memory access independent of the current context.
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