Alec Ibarra

Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints.

For more than a decade, the industry has watched Google build an empire on its own silicon. Search, Ads, YouTube, and every generation of Gemini run on TPUs. Few accelerators have attracted as much architectural scrutiny or as much debate about what their performance and economics would look like outside the company that designed them. Anthropic being the biggest user of TPUs, surpassing Deepmind’s own use by 2029. Google’s internal success was never the question.