The paper argues that we have been wasting a lot of expensive GPU cycles by forcing transformers to relearn static things like names or common phrases through deep computation. Standard models do not have a way to just look something up so they end up simulating memory by passing tokens through layer after layer of feed forward networks. DeepSeek introduced a module called Engram which adds a dedicated lookup step for local N-gram patterns. It acts like a new way to scale a model that is separate from the usual compute heavy Mixture of Experts approach.

The architecture uses multi head hashing to grab static embeddings for specific token sequences which are then filtered through a context aware gate to make sure they actually fit the current situation. They found a U shaped scaling law where the best performance happens when you split your parameter budget between neural computation and this static memory. By letting the memory handle the simple local associations the model can effectively act like it is deeper because the early layers are not bogged down with basic reconstruction.

One of the best bits is how they handle hardware constraints by offloading the massive lookup tables to host RAM. Since these lookups are deterministic based on the input tokens the system can prefetch the data from the CPU memory before the GPU even needs it. This means you can scale to tens of billions of extra parameters with almost zero impact on speed since the retrieval happens while the previous layers are still calculating.

The benchmarks show that this pays off across the board especially in long context tasks where the model needs its attention focused on global details rather than local phrases. It turns out that even in math and coding the model gets a boost because it is no longer wasting its internal reasoning depth on things that should just be in a lookup table. Moving forward this kind of conditional memory could be a standard part of sparse models because it bypasses the physical memory limits of current hardware.