Alphabet (GOOGL) shares climbed about 3% on Monday after a report said Google is developing a new AI server chip designed specifically to run its Gemini models more efficiently.
According to The Information, the chip, internally codenamed “Frozen v2,” would permanently embed parts of Gemini’s architecture into the silicon.
The design is intended to reduce the amount of computation and data movement required to process AI queries, potentially improving efficiency while lowering power consumption.
Alphabet responded to the report by emphasizing its ongoing investment in AI hardware innovation.
Its teams are “constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers” and “while not every project moves into production, this rigorous exploration is central to our full stack approach.”
“By co-designing our hardware and software from the ground up, we ensure our systems are integrated and highly optimized for real-world workloads,” continued the statement.
The stock was trading 1.3% higher at the time of writing.
Frozen v2 targets greater AI efficiency
Unlike Google’s Tensor Processing Units (TPUs), which are designed to support a broad range of artificial intelligence models, Frozen v2 is reportedly being built exclusively for Gemini.
According to The Information, Google engineers believe the chip could deliver between six and ten times more tokens per unit of power than the company’s latest TPUs.
Rather than replacing Google’s general-purpose AI processors, Frozen v2 is expected to become a specialized addition to the company’s custom-chip portfolio.
The report said Google is targeting deployment around 2028, with the project intended to help address internal compute shortages that have reportedly limited the company’s cloud capacity.
Last month, Google reportedly agreed to pay SpaceX nearly $1 billion per month to help meet enterprise computing commitments.
The trade-off, according to the report, is flexibility.
Frozen v2 would remain effective only if future Gemini models continue using the same underlying architecture.
Google reportedly views the project as partly a trial run and does not expect to manufacture the chips at the same scale as its TPUs.
AI competition continues to intensify
Google’s hardware ambitions come as its AI business faces growing competitive pressure.
Recently, a Bloomberg report said the next Gemini Pro release has been delayed.
Google has also lost several senior researchers to competitors.
Chinese AI developers have also gained traction, with their models now accounting for 45% of token usage among US companies.
Recent releases from Moonshot AI and Alibaba have further narrowed the performance gap with leading US AI models.
Meanwhile, Google DeepMind Chief Executive Demis Hassabis is on Capitol Hill this week advocating for a federally overseen, industry-funded AI watchdog modeled after FINRA to evaluate advanced AI systems for national security risks before deployment.
Wall Street remains constructive on Alphabet
Google continues expanding its in-house AI hardware efforts as it seeks to reduce dependence on Nvidia while lowering the cost of running Gemini.
Earlier this year, the company introduced its eighth-generation TPU and has increasingly marketed its chips to external cloud customers, including a multibillion-dollar agreement to supply TPUs to Meta Platforms.
Google has also approached other cloud providers that have traditionally relied on Nvidia GPUs.
Separately, BMO Capital raised its price target on Alphabet to $455 from $435 while maintaining an Outperform rating.
The firm increased its Google Cloud estimates for the fourth quarter and fiscal 2027, citing stronger cloud demand, expanding capacity and a substantial backlog.
However, BMO also noted that questions remain regarding Gemini model performance following reports that Gemini Pro 3.5 has been delayed as it falls short on certain benchmarks.
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