TLDRs;
- Google unveils Ironwood, its fastest TPU yet, boasting 4x performance gains and massive pod-scale computing capabilities.
- Anthropic to deploy one million Ironwood TPUs to power Claude, bolstering Google Cloud’s AI infrastructure footprint.
- Pricing and benchmarks remain undisclosed, leaving uncertainty about Ironwood’s competitiveness against Nvidia’s H100/H200 GPUs.
- Ironwood aims to simplify model migration via PyTorch and JAX integration, improving inference speeds up to 5x.
Google is escalating the battle for AI dominance with the public launch of its seventh-generation Tensor Processing Unit (TPU), codenamed Ironwood.
The custom-built chip, designed to power and train large-scale AI models, represents Google’s most ambitious hardware release yet, and a direct challenge to Nvidia’s long-standing supremacy in the AI chip market.
Initially unveiled for limited testing in April, Ironwood TPUs will soon be accessible to developers and enterprise customers through Google Cloud. The company touts the chip as being over four times faster than its predecessor, with up to 9,216 interconnected units per pod, a configuration capable of delivering a staggering 42.5 exaflops of floating-point performance.
This rollout comes as Google intensifies competition with Amazon Web Services (AWS), Microsoft Azure, and Nvidia, all of which dominate the high-performance AI infrastructure sector.
Anthropic Bets Big on Ironwood
One of the first major adopters of Ironwood is Anthropic, the AI startup behind the Claude chatbot. According to Google, Anthropic plans to harness up to one million Ironwood TPUs to train and deploy its next-generation AI models. This partnership could significantly amplify Google’s cloud business while providing Anthropic with enhanced efficiency and scalability.
Google’s Q3 2025 earnings report reflects strong momentum behind this strategy, with cloud revenue surging 34% year-over-year to $15.2 billion. The company has also increased its capital expenditure forecast to $93 billion, signaling heavy investment in AI computing infrastructure.
“Ironwood is designed for the AI era,” a Google spokesperson noted, emphasizing the chip’s balance of performance, energy efficiency, and integration flexibility. Google claims Ironwood delivers nearly double the power efficiency of its prior TPU generation, an increasingly critical factor amid soaring energy demands from AI data centers.
Performance Questions Still Linger
Despite the bold claims, Ironwood’s real-world value remains untested against Nvidia’s H100 and H200 GPUs, the current industry standards for AI training and inference. Google has yet to disclose pricing details or provide MLPerf benchmarks, leaving enterprises uncertain about its total cost of ownership (TCO) compared to existing GPU options.
Historically, Google’s TPU v5e and v5p models were priced at $1.20 and $4.20 per chip-hour, respectively. Analysts expect Ironwood to command a premium, given its scale and architecture. Industry observers say cost-per-token metrics will ultimately determine whether Ironwood can compete effectively with Nvidia’s pricing models.
Still, Ironwood’s performance potential is undeniable. Each TPU pod’s massive interconnectivity allows for faster training times and higher throughput, ideal for workloads such as large language model (LLM) development and real-time AI applications like voice assistants, image recognition, and chatbots.
Bridging the Gap with PyTorch and XLA
To ease adoption, Google is improving compatibility between Ironwood and popular machine learning frameworks such as PyTorch and JAX.
Through its unified vLLM TPU backend, developers can now deploy AI models without modifying existing code, potentially improving inference throughput by up to 5x.
However, challenges persist for model training, particularly with PyTorch/XLA integration, where teams still face multiprocessing and dynamic shape recompilation issues. Google expects that MLOps specialists and cloud consultants will play a crucial role in helping enterprises migrate smoothly to the new TPU architecture.
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