Back to Devoured - July 21, 2026
DEVOURED
Google's New Chip for Gemini

Google's New Chip for Gemini

AI TechCrunch
Google is reportedly developing a custom server chip, 'Frozen v2,' aiming for a 6-10x increase in token generation efficiency per unit of power.
What: The chip, expected by 2028, seeks to lower inference costs for Gemini models and reduce dependency on Nvidia hardware, as Google navigates a massive $180-$190 billion AI investment roadmap.
Why it matters: Inference efficiency is becoming the primary metric for long-term AI sustainability; custom silicon is now the only way for hyperscalers to maintain profit margins as model scales grow.
Deep dive
  • Efficiency Metric: The goal is to maximize 'tokens per unit of power,' a critical KPI for large-scale model operation.
  • Market Pressure: Alphabet is seeking to reassure investors amid high capital expenditure requirements for AI infrastructure.
  • Industry Context: Similar moves are being made by OpenAI (Jalapeño) and Anthropic (Samsung partnership) to vertically integrate their AI hardware stack.
Decoder
  • Inference: The process of running a trained machine learning model to make predictions or generate content based on new data.
Original article

Alphabet, Google’s parent company, is designing a new server chip to help its in-house Gemini models operate more efficiently.

The new chip, internally dubbed “Frozen v2,” is slated to be released sometime in 2028, The Information reported, citing anonymous sources. According to the report, the chip 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.

In a response to TechCrunch, the company didn’t directly confirm the report. It didn’t deny it either.

“Our teams are constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers,” Google 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.”

AI companies have increasingly sought to produce their own chips as a way to make their in-house models run more efficiently and to address global shortages in AI computing capacity. Such efficiency has become a key selling point for tech companies as concerns about AI spend have dampened the market euphoria that previously characterized the industry. At the same time, firms are engaged in an ongoing attempt to wean themselves off chipmaker Nvidia, which has historically dominated the AI chip market and whose dominance has left major AI makers dependent on its hardware.

In June, OpenAI announced its first custom chip, an inference processor dubbed Jalapeño. Earlier this month, it was reported that Anthropic was discussing a new chipmaking partnership with Samsung.

Investors have previously worried about Alphabet’s massive planned expenditures designed to help it build out its AI strategy. Earlier this year, Google said that it plans to spend between $180 billion and $190 billion. With so much money at stake, the company needs to prove that those investments will pay off.

News of the more efficient Frozen v2 chip appears to have assuaged investors, giving Google a boost ahead of its earnings report later this week. Following publication of The Information’s report, the company’s stock climbed some 3% on Monday morning.

Explore all 43 articles in today's digest

Fresh dev news, AI-digested daily. Never miss a trend at devoured.fyi