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Google TPU remains the only widely successful AI ASIC in the current market
Saturday, March 7, 2026 at 06:11 AM
The author notes that among custom AI ASICs, only Google's TPU has achieved significant market success, suggesting that most observers lack a deep understanding of the underlying software frameworks like JAX that drive these hardware efficiencies.
Context
While several hyperscalers have attempted to break NVIDIA's market dominance with custom silicon, the Google TPU currently stands as the only widely successful AI ASIC. In late 2025, Google further solidified this lead by launching Ironwood, its seventh-generation TPU designed specifically for the 'age of inference.' This new hardware scales to 9,216 chips and delivers a massive 42.5 Exaflops of compute power, which Google claims makes it more powerful than the world's largest supercomputer.
A key driver of this success is the JAX framework, which provides a native structural advantage for TPUs that competing frameworks cannot easily replicate. While Amazon and Microsoft have recently accelerated their own efforts with Trainium3 and Maia 200, Google’s long-term integration of hardware and software has resulted in significant business results. In Q4 2025, Alphabet reported annual revenues exceeding $400 billion for the first time, fueled by a 48% acceleration in Cloud revenue as customers increasingly adopt TPU-driven AI infrastructure.
Sources (9)
Amazon's push to make AI cheaper — and why it matters to reigniting the stockAlphabet earnings, Q4 2025: CEO's remarks - Google BlogML Engineer comparison of Pytorch, TensorFlow, JAX, and FlaxChoosing Your AI Stack: PyTorch, TensorFlow, or JAX?
Building production AI on Google Cloud TPUs with JAX
- Google Developers Blog
GPU vs TPU: How to Choose the Right Hardware for Your AI Projects - FluenceComparing PyTorch and JAX | DigitalOceanAI Accelerator - AWS Trainium - AWS
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