Engram uses N-gram embedding tables to bypass early layer computation
August 27, 2026
Engram indexes vectors by 2-3 token N-grams rather than single IDs, enabling O(1) retrieval of memorized multi-token entities. This method bypasses the transformer layers typically used to reconstruct static phrases and collocations, reducing required FLOPs for common sequences.
HOW THIS AFFECTS YOU
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builderThis approach could reduce inference costs for models handling highly repetitive text patterns.
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researcherYou can explore N-gram indexing as a method to offload early-layer attention tasks.