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Running state-of-the-art AI models requires an astounding amount of power, and processing every prompt—a step known as inference—quickly adds up to massive electricity bills. Google, however, might be on the verge of a massive breakthrough that redefines how efficiently its models run. According to a report, Alphabet—Google’s parent company—is internally developing a custom server chip codenamed “Frozen v2” that could bring up to 10x efficiency on Gemini AI models.
The Information reports that the chip points to a potential 2028 release. Performance-wise, it could make running Gemini 6 to 10x times more efficient than Google’s current AI accelerators, measured by tokens generated per unit of power. A tenfold leap in efficiency would completely change the economics of running generative AI at scale, allowing Google to serve complex queries at a fraction of today’s operational cost.
Baking software directly into the hardware
Achieving a 10x efficiency surge requires more than just pushing traditional silicon faster. Google’s design strategy relies on deep architectural integration, effectively baking parts of Gemini’s core neural software structure directly into the physical chip.
By hardcoding AI routines straight into hardware, the chip eliminates unnecessary calculations and minimizes continuous data movement across server racks—the exact bottlenecks that drain immense amounts of power during heavy workloads.
Google declined to confirm specific specs or release schedules. However, a spokesperson told TechCrunch that co-designing hardware and software from the ground up is key to achieving maximum system performance.
This custom silicon approach also allows Google to reduce its reliance on third-party suppliers like Nvidia. This way, the firm would join a broader industry trend seen in OpenAI’s custom “Jalapeño” chip and Anthropic’s hardware talks with Samsung.
Soothing investor worries over server spending
A 10-fold efficiency boost arrives at a critical moment for Alphabet’s bottom line. The company plans to spend between $180 billion and $190 billion on capital expenditures in 2026 alone. This eye-popping figure has raised eyebrows among investors concerned about long-term AI profitability.
Plus, severe compute capacity shortages recently forced Google Cloud to turn away select enterprise orders. Developing ultra-efficient internal hardware directly addresses that bottleneck, allowing existing data centers to process significantly more workloads without overloading local electric grids.
Wall Street reacted positively to the prospect of a 10x efficiency gain. Alphabet stock rose roughly 3% following the report ahead of its quarterly earnings release.
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