Google has made its seventh‑generation Tensor Processing Unit, Ironwood, available to the public, marking a significant step in the company’s push to compete with Nvidia’s GPUs for AI training and inference workloads.
Ironwood delivers 4,614 FP8 TFLOPS per chip, 192 GB of HBM3E memory, and a bandwidth of 7.37 TB/s. Pods can scale to 9,216 accelerators, yielding a total of 42.5 FP8 exaFLOPS for training and inference, and the chip is more than four times faster than its predecessor while offering a 10‑fold peak performance improvement over TPU v5p.
The launch strengthens Google Cloud’s AI portfolio and supports the company’s “age of inference” strategy, where real‑time model deployment is critical. By integrating custom silicon with its AI models and cloud services, Google can deliver higher performance at lower energy cost, a key differentiator against Nvidia’s GPU‑centric approach.
Amin Vahdat, VP/GM of AI & Infrastructure, and Mark Lohmeyer, VP & GM of Compute and AI Infrastructure, said the new silicon “offers a 10X peak performance improvement over TPU v5p and more than 4X better performance per chip for both training and inference workloads compared to TPU v6e (Trillium), making Ironwood our most powerful and energy‑efficient custom silicon to date.”
Analysts and investors have reacted positively, viewing the announcement as a direct challenge to Nvidia’s dominance. The performance gains, scalability, and energy efficiency of Ironwood are expected to attract large AI workloads, including Anthropic’s plan to deploy up to one million Ironwood TPUs for its Claude model, and to increase Google Cloud’s share of the rapidly growing AI compute market.
The introduction of Ironwood is likely to boost Google Cloud’s revenue from AI infrastructure, enhance its competitive moat, and accelerate the adoption of custom silicon across enterprise and research customers, positioning Alphabet for long‑term growth in the AI‑driven economy.
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