Google is building a chip with Gemini baked into the silicon
TLDR ● Covered by 6 sources
Google is reportedly developing a chip called Frozen v2 that would have Gemini's neural-network architecture physically etched into the silicon rather than stored in memory, potentially achieving 6 to 10 times greater efficiency than current custom AI chips and targeting deployment as early as 2028. The project reflects Google's effort to reduce reliance on Nvidia and address internal capacity constraints, trading flexibility for speed and power efficiency. If successful, this approach could force competitors to develop similar custom silicon solutions optimized for specific models rather than general-purpose hardware.
Why it matters
Google is reportedly working on a project that would bake Gemini's neural-network architecture right into the circuitry of a chip, locking the model into the chip to increase efficiency and decrease overhead while still allowing new weights to be loaded.
Also covered by
- Google DeepMind — Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
- The New Stack — Google ships 3 new Gemini models. Just not the one everyone’s waiting for.
- The Verge — Google launches a cheaper alternative to large AI security models like Mythos
- The New Stack — Google just bet its inference future on a chip built for one model
- TechCrunch AI — Google is working on a new AI chip designed to make Gemini more efficient
