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AI infrastructure demand strains global supply chains already operating near limits
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- Meta explores new hardware paths while cloud suppliers race to secure capacity
- Google positions its TPUs as a credible option for large deployments
- Data-center operators face rising component costs across multiple hardware categories
Meta is reported to be in advanced discussions to secure large quantities of Google’s custom AI hardware for future development work.
The negotiations revolve around renting Google Cloud Tensor Processing Units (TPUs) during 2026 and transitioning to direct purchases in 2027.
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This is a shift for both companies, as Google has historically limited its TPUs to internal workloads while Meta has relied on a wide mix of CPUs and GPUs sourced from multiple vendors.
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Meta is also exploring broader hardware options, including interest in RISC-V-based processors from Rivos, suggesting a wider move to diversify its compute base.
The possibility of a multibillion-dollar agreement caused immediate market changes, with Alphabet’s valuation climbing sharply, putting it close to the $4 trillion mark, while Meta also saw its stock rise following the reports.
Nvidia’s stock declined by several percentage points as investors speculated about the long-term effect of major cloud providers shifting their spending to alternative architectures.
Estimates from Google Cloud executives suggest a successful deal could allow Google to capture a meaningful share of Nvidia’s data-center revenue, which exceeds $50 billion in a single quarter this year.
Are you a pro? Subscribe to our newsletterContact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.The scale of demand for AI tools has created intense competition for supply, raising questions about how new hardware partnerships could influence sector stability.
Even if the deal proceeds as planned, it will enter a market that remains constrained by limited fabrication capacity and aggressive deployment timelines.
Data center operators continue to report shortages in GPUs and memory modules, with prices projected to rise through next year.
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The rapid expansion of AI infrastructure has strained logistics chains for every major component, and current trends suggest that procurement pressures may intensify as companies race to secure long-term hardware commitments.
These factors create uncertainty around the actual impact of the deal, since the broader supply environment may limit production volume regardless of financial investment.
Analysts caution that the future performance of any of these architectures remains unclear.
Google maintains an annual release schedule for its TPUs, while Nvidia continues to iterate on its own designs with equal speed.
The competitive landscape may shift again before Meta receives its first large shipment of hardware.
There is also the question of whether alternative designs can offer longer operational value than existing GPUs.
The rapid evolution of AI workloads means device relevance can change dramatically, and these dynamics show why companies continue to diversify their compute strategies and explore multiple architectures.
Via Tom's Hardware
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TOPICS Google AI
Efosa UdinmwenFreelance JournalistEfosa has been writing about technology for over 7 years, initially driven by curiosity but now fueled by a strong passion for the field. He holds both a Master's and a PhD in sciences, which provided him with a solid foundation in analytical thinking.
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Meta may spend billions to acquire promising AI accelerator startup to loosen reliance on Nvidia by supercharging its own MTIA AI chip - but what will Jensen say?
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