Engram embeddings: caching multi-token meanings to cut compute and boost intelligence
bookwormengr · x · 2026-09-10
bookwormengr explains why Engram embeddings work: embeddings store token meanings, but many concepts span multiple tokens ("Alexander the great," not "Alexander the barista"), forcing lower transformer layers to waste compute reconstructing sequence meaning.
Engram precomputes and stores embeddings for 2–3 token groups, retrieved at runtime. They can live in host memory (LPDDR) and load while GPUs compute, so fetch time overlaps with compute.
- Saves substantial compute and backbone parameters
- Promises higher intelligence at lower parameter counts
- Details in the referenced paper
Related event: Engram Embeddings: Caching Multi-Word Semantics for Cheaper, Smarter Models(3 posts)→
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