Google is developing a new server chip designed to run its Gemini AI models more efficiently by integrating parts of the model directly into the hardware, according to a report by The Information. The initiative is aimed at improving AI performance while addressing growing computing capacity challenges.
The report states that the new chip, informally known as “Frozen v2,” is expected to help Google manage the increasing demand for AI computing resources. The capacity constraints have reportedly led to internal tensions and prompted Google Cloud to turn down some deals with external customers.
According to the report, Google plans to deploy the new chip as early as 2028. Engineers are still finalizing the chip’s design, including determining how much of the Gemini model’s information will be embedded directly into the hardware.
The chip is expected to be 6–10x more efficient than Google’s latest custom AI chips when measured by the number of AI tokens processed per unit of power. This improvement could significantly enhance the efficiency of serving AI models at scale.
Responding to the report, a Google Cloud spokesperson said, “Our teams are constantly researching and experimenting with new innovations… By co-designing our hardware and software from the ground up, we ensure our systems are integrated and highly optimized.”
The report also noted that the “Frozen” project is intended to create a new family of in-house AI chips alongside Google’s existing Tensor Processing Units (TPUs), rather than replace them.
The development follows a recent Bloomberg report stating that Google delayed the launch of its latest Gemini AI model after it did not meet internal performance goals, particularly in coding capabilities.
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