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According to Reuters, citing The Information, Google will collaborate with MediaTek to develop its seventh-generation Tensor Processing Unit (TPU), which is also known as TPU v7. Google maintains its existing partnership with Broadcom despite the new MediaTek collaboration. The AI accelerator is scheduled for production in 2026, and TSMC is handling manufacturing duties. Google will lead the core architecture design while MediaTek manages I/O and peripheral components, as Economic Daily News reports. This differs from Google's ongoing relationship with Broadcom, which co-develops core TPU architecture. The MediaTek partnership reportedly stems from the company's strong TSMC relationship and lower costs compared to Broadcom.
There is also a possibility that MediaTek could design inference-focused TPU v7 chips while Broadcom focuses on training architecture. Nonetheless, the development of TPU is a massive market as Google is using so many chips that it could use a third company, hypothetically. The development of TPU continues Google's vertical integration strategy for AI infrastructure. Google reduces dependency on NVIDIA hardware by designing proprietary AI chips for internal R&D and cloud operations. At the same time, competitors like OpenAI, Anthropic, and Meta rely heavily on NVIDIA's processors for AI training and inference. At Google's scale, serving billions of queries a day, designing custom chips makes sense from both financial and technological sides. As Google develops its own specific workloads, translating that into hardware acceleration is the game that Google has been playing for years now.
View at TechPowerUp Main Site | Source
There is also a possibility that MediaTek could design inference-focused TPU v7 chips while Broadcom focuses on training architecture. Nonetheless, the development of TPU is a massive market as Google is using so many chips that it could use a third company, hypothetically. The development of TPU continues Google's vertical integration strategy for AI infrastructure. Google reduces dependency on NVIDIA hardware by designing proprietary AI chips for internal R&D and cloud operations. At the same time, competitors like OpenAI, Anthropic, and Meta rely heavily on NVIDIA's processors for AI training and inference. At Google's scale, serving billions of queries a day, designing custom chips makes sense from both financial and technological sides. As Google develops its own specific workloads, translating that into hardware acceleration is the game that Google has been playing for years now.

View at TechPowerUp Main Site | Source