<div id="readability-page-1"><div id="ArticleBody-InlineImage-108295064"><div><div>Google CEO Sundar Pichai gestures during a meeting with France's President Emmanuel Macron on the sidelines of the AI Impact Summit in New Delhi on Feb. 19, 2026.Ludovic Marin | Afp | Getty Images</div></div></div><div>After years of producing chips that can both train artificial intelligence models and handle inference work, Google is separating those tasks into distinct processors, its latest effort to take on Nvidia in AI hardware.Google said Wednesday that it's making the change for the eighth generation of its tensor processing unit, or TPU. Both chips will become available later this year. &#34;With the rise of AI agents, we determined the community would benefit from chips individually specialized to the needs of training and serving,&#34; Amin Vahdat, a Google senior vice president and chief technologist for AI and infrastructure, said in a blog post. In March, Nvidia talked up forthcoming silicon that can enable models to rapidly respond to users' questions, thanks to technology obtained in its $20 billion deal with chip startup Groq. Google is a large Nvidia customer, but offers TPUs as an alternative for companies that use its cloud services. Most of the world's top technology companies are pursuing custom semiconductor development for artificial intelligence to maximize efficiency and so they can build for specialized use cases. Apple has included neural engine AI components in its in-house iPhone chips for years. Microsoft announced a second-generation AI chip in January. Last week, Meta said it's working with Broadcom to develop multiple versions of AI processors.Google was early to the trend. In 2015, the company started using processors it had designed for running AI models, and began renting them to cloud clients in 2018. Amazon Web Services announced the Inferentia chip for handling AI requests in 2018, and unveiled the Trainium processor for training AI models in 2020. DA Davidson analysts estimated in September that the TPU business, coupled with the Google DeepMind AI group, would be worth about $900 billion.None of the tech giants are displacing Nvidia, and Google isn't even comparing the performance of its new chips with those from the AI chip leader. Google did say the training chip enables 2.8 times the performance of the seventh-generation Ironwood TPU, announced in November, for the same price, while performance is 80% better for the inference processor.Nvidia said its upcoming Groq 3 LPU hardware will draw on large quantities of static random-access memory, or SRAM, which is used by Cerebras, an AI chipmaker that filed to go public earlier this month, Google's new inference chip, dubbed TPU 8i, also relies on SRAM. Each chip contains 384 MB of SRAM, triple the amount in Ironwood.The architecture is designed &#34;to deliver the massive throughput and low latency needed to concurrently run millions of agents cost-effectively,&#34; Sundar Pichai, CEO of Google parent Alphabet, wrote in a blog post.Adoption of Google's AI chips is ramping up. Citadel Securities built quantitative research software that draws on Google's TPUs, and all 17 U.S. Energy Department national laboratories use AI co-scientist software built on the chips, Google said. Anthropic has committed to using multiple gigawatts worth of Google TPUs.WATCH: Broadcom agrees to expanded chip deal with Google, Anthropic</div><div>Choose CNBC as your preferred source on Google and never miss a moment from the most trusted name in business news.</div></div>

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Google has introduced dedicated chips for AI training and inference, aiming to compete with Nvidia. The eighth generation of its tensor processing unit (TPU) will feature separate processors for these tasks, enhancing performance significantly. The training chip boasts 2.8 times the performance of its predecessor, while the inference processor shows an 80% improvement. Both chips will be available later this year, with a focus on maximizing efficiency for AI applications. Adoption of Google's TPUs is increasing, with major clients including Citadel Securities and the U.S. Energy Department's national laboratories.

- Google is launching separate processors for AI training and inference with its eighth generation of tensor processing units (TPUs) to compete with Nvidia.  
- The new chips are expected to deliver significant performance improvements over the previous generation and will utilize SRAM technology for enhanced efficiency.  
- Adoption of Google's AI chips is increasing, with major organizations like Citadel Securities and the U.S. Energy Department utilizing them for advanced AI tasks.

Google unveils chips for AI training and inference in latest shot at Nvidia