Google develops 'Frozen v2' chip to hardwire Gemini architecture into silicon

The shift from general-purpose AI accelerators to model-specific silicon marks a critical phase in infrastructure consolidation. By 'freezing' Gemini's architecture into hardware, Google can bypass the energy efficiency ceiling of traditional GPUs, which must support multiple model types. This vertical integration provides a major cost advantage for high-volume inference, potentially lowering the barrier for real-time video processing and multimodal streaming applications. For the broader ecosystem, it signals a move away from the merchant silicon bottleneck as hyperscalers trade architectural flexibility for massive operational savings. Watch for Google to prioritize those Gemini-native workloads that currently strain cloud capacity, such as 24/7 metadata generation and dynamic ad insertion.

The development of Frozen v2 arrives as Google aggressively expands its custom silicon portfolio to counter Nvidia's dominance. In April 2026, Broadcom confirmed it reached an expanded long-term agreement with Google to supply custom AI chip components through 2031, per CNBC. This followed the general availability of Ironwood (TPU v7) in late 2025, which Google industry analysts at Intuition Labs reported delivering a 4x performance jump over previous Trillium chips. These hardware cycles are increasingly vital as Google faces internal compute shortages that have reportedly forced its cloud division to reject certain enterprise deals, according to The Information in July 2026.

Google is also diversifying beyond pure AI accelerators with general-purpose custom hardware. Per Shacknews, Google's Arm-based Axion CPU reached general availability in late 2024 to power high-traffic internal services like YouTube Advertising and BigQuery. This multi-pronged hardware strategy mirrors shifts at rival hyperscalers; Microsoft announced its second-generation Maia 200 AI chip in January 2026, specifically targeting inference efficiency for OpenAI's GPT models, according to GeekWire. Meanwhile, Amazon is exploring merchant sales of its Trainium processors as of June 2026 to capitalize on external data center demand, per Bloomberg.

Technically, the 'Frozen' approach signals a move toward Application-Specific Integrated Circuits (ASICs) that are even more specialized than standard TPUs. Whereas TPUs are flexible across various neural network architectures, Frozen v2 seeks to optimize Gemini’s specific math operations. This 'software-to-silicon' pipeline is supported by a deepenening relationship with Samsung; according to a July 2026 report from CryptoBriefing, Google is in talks with Samsung to use its 2nm process for a related component codenamed Icefish, aimed at further reducing power consumption across its next-generation AI server racks.