The best engram explainer yet argues they buy depth, not SSD-resident 1T models.

A 1,134-upvote r/LocalLLaMA post pushes back on the claim that n-gram tables let you run 1T+ models with 980B parameters offloaded to SSD (r/LocalLLaMA). An engram is an embedding table keyed on the last two or three tokens rather than one token ID, so "New York" gets a memorized vector via an O(1) hash lookup with no FLOPs, freeing early transformer layers from re-deriving multi-token entities. Because lookups cost no compute, you can quantize weights to Q4_K_XL while keeping the engram table at native precision. Commenters report the practical tell is better letter counting with minimal reasoning and better negation handling.