GoKawiilGoogle announced two new tensor processing units (TPUs) for the "agentic era," with separate processors dedicated to training and inference. "With the rise of AI agents, we determined the community would benefit from chips individually specialized to the needs of training and serving," Amin Vahdat, a Google senior vice president and chief technologist for AI and infrastructure, said in a blog post. Both chips will become available later this year. CNBC reports: 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. [...] 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 megabytes of SRAM, triple the amount in Ironwood. The architecture is designed "to deliver the massive throughput and low latency needed to concurrently run millions of agents cost-effectively," Sundar Pichai, CEO of Google parent Alphabet, wrote in a blog post.
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