Google working on new AI chip for Gemini efficiency

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By NewSunSEO July 24, 2026

Alphabet, Google’s parent company, is designing a new server chip, internally dubbed “Frozen v2,” that is aimed at making its in-house Gemini AI models run more efficiently when serving queries. According to a report from The Information, the chip is slated for release sometime in 2028 and could be between six and 10 times more efficient than Google’s existing AI chips, measured by the number of tokens generated per unit of power.

Google declined to directly confirm the report but did not deny it. “Our teams are constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers,” a Google spokesperson told TechCrunch. “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.”

What is Frozen v2 and what would it do?

Frozen v2 is being designed to serve Gemini, rather than to train it. The efficiency gains reported by The Information, a six-to-10x improvement over Google’s current Tensor Processing Units (TPUs), are measured in tokens generated per unit of power during inference, the stage where a model produces answers in response to user queries.

The shift signals a move toward more specialized AI hardware. Instead of treating Gemini as software running on general-purpose AI accelerators, Google appears to be co-designing parts of the model and the chip so that specific workloads map directly onto the silicon. Investors reacted positively: Alphabet’s stock climbed roughly 3% on Monday morning following The Information’s report, ahead of the company’s earnings release later that week.

Why is Google building its own AI chips?

Frozen v2 is primarily aimed at easing Google’s internal compute crunch rather than being sold to outside customers. Several factors are pushing AI companies to bring more of their silicon in-house:

Cost pressure has made serving-cost a more prominent selling point as concerns about AI spending cool some of the market enthusiasm seen in prior years.
Owning custom silicon can help secure more AI compute capacity.
Reducing dependence on Nvidia. Major AI makers have worked to diversify away from Nvidia, which has historically dominated the AI chip market.

Google’s capital plans highlight what is at risk. Earlier in 2026, the company said it intends to spend between $180 billion and $190 billion on infrastructure tied to its AI strategy, a figure investors have watched closely given concerns about returns on that investment.

How does this fit into the broader AI chip race?

Google is one of several major AI companies moving toward custom silicon. OpenAI announced its first custom chip in June, an inference processor called Jalapeño, and earlier in July it was reported that Anthropic was discussing a new chipmaking partnership with Samsung. Google’s existing TPUs have long given it a degree of self-sufficiency, and Frozen v2 would extend that approach further by tailoring the hardware specifically to Gemini.

Because the chip is not expected until 2028, it is a longer-horizon bet rather than an immediate replacement for the hardware currently serving Gemini. If it lands as described, however, it could give Google a structural cost advantage on inference, the most power-intensive part of running an AI product at scale.

FAQ

What is Google’s Frozen v2 chip?

Frozen v2 is the internal name for a server chip Alphabet is designing to make its Gemini AI models more efficient when serving user queries. It is expected to arrive in 2028.

How much more efficient is Frozen v2 expected to be?

According to The Information, citing anonymous sources, Frozen v2 could be between six and 10 times more efficient than Google’s current TPUs, measured by tokens generated per unit of power.

Why is Google making its own AI chips instead of relying on Nvidia?

Google is bringing more silicon in-house to address internal compute shortages, reduce the cost of serving AI responses, and lessen its dependence on Nvidia, which has historically dominated the AI chip market.