Google to Embed Gemini Architecture in New AI Chip, Boosting Inference Efficiency by Up to 10x

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This week, AI and crypto news broke that Google is developing the Frozen v2 AI inference chip. The new design will embed part of the Gemini architecture directly into the hardware, aiming to reduce computational and data movement overhead. Google estimates the chip could process 6 to 10 times more tokens per watt than the latest TPU. The company plans to deploy it by 2028 to alleviate compute shortages—Google Cloud has already begun rejecting external customer orders due to this bottleneck. The Frozen v2 will operate alongside TPU, offering greater efficiency but reduced flexibility; if Gemini’s architecture evolves, these chips may lose value. New token listings remain under close scrutiny as the AI landscape continues to evolve.

According to Beating Monitor, Google is reportedly developing the Frozen v2 AI inference chip. It will directly hardwire parts of the Gemini architecture into the chip, reducing computation and data movement during model execution. Internally, it is expected to process 6 to 10 times more tokens per watt than the latest TPU. Google plans to deploy it as early as 2028. The company aims to alleviate its computing capacity shortage, which has already forced Google Cloud to turn away some external customer orders. Frozen v2 will coexist alongside TPU; while TPU can run various models, Frozen v2 trades flexibility for efficiency. This also represents an architectural bet—if Gemini undergoes major changes to its underlying design, these chips may no longer be usable.

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