Meta is dramatically expanding its AI infrastructure footprint with a multiyear Nvidia deal that will see the company deploy millions of Nvidia chips across its artificial intelligence data centers. The partnership, announced Tuesday, Feb. 17, 2026, spans far more than GPUs—covering Nvidia’s Grace CPUs, future Vera CPUs, Rubin-generation AI chips, networking, and security tech.
In a statement, Meta CEO Mark Zuckerberg framed the move as part of the company’s push to “deliver personal superintelligence to everyone in the world,” echoing the vision he laid out in July 2025. Neither company disclosed financial terms, though analysts expect the spend to land deep in the tens-of-billions range given Meta’s massive capex guidance for the year.
👉 Browse “Designing Data-Intensive Applications” on Amazon
(Affiliate link: using this link may earn a commission.)
Meta’s AI spending sets the stage
Meta has already signaled just how aggressive it plans to be. In January, the company said it expects 2026 capex of roughly $115B–$135B, driven largely by AI infrastructure. That context makes Nvidia the obvious beneficiary: even a “normal” year for Meta is massive by data-center standards, and 2026 isn’t shaping up to be normal.
The deal also comes amid heightened investor sensitivity around AI supply chains. Nvidia remains the dominant supplier for training and inference at scale, but the industry has been watching for signs that hyperscalers are seriously diversifying—whether through in-house silicon, AMD, or alternative accelerators.
The standout: Grace as a standalone CPU rollout
The biggest architectural shift in this partnership is CPU-centric. Meta will be the first to deploy Nvidia Grace CPUs as standalone chips at large scale, rather than only in GPU-paired configurations. Nvidia is positioning that as its first major production deployment of Grace “on its own,” which matters because it suggests Grace is moving from “interesting component” to “infrastructure pillar.”
Why now? The simplest explanation is that AI data centers are no longer “GPU farms with some CPUs attached.” Inference-heavy and agentic workloads increasingly need balanced systems—fast CPU orchestration, efficient data movement, and predictable performance per watt. Meta scaling standalone Grace effectively endorses Nvidia’s attempt to sell the entire rack, not just the accelerator.
Vera and Rubin: what Meta is lining up next
Meta’s roadmap goes beyond what it can deploy today. The company plans to deploy Vera CPUs starting in 2027, while also securing supply of Nvidia’s current Blackwell generation and next-gen Rubin AI chips. Nvidia’s latest platforms have been demand-constrained, with Blackwell widely reported to be back-ordered and Rubin now entering production—so locking in capacity is itself a strategic win.
The broader message is clear: Meta wants predictable access to Nvidia’s pipeline across multiple generations, instead of fighting for allocation quarter by quarter.
Networking and “security” are part of the package
This deal isn’t just about compute. Meta will also use Nvidia’s Spectrum-X Ethernet networking technology—critical for linking large numbers of GPUs inside AI clusters without bottlenecks becoming the real limiter.
On the security side, Meta is also adopting Nvidia’s Confidential Computing capabilities in support of AI features for WhatsApp—technology designed to protect data while it’s being processed, not only while it’s stored or in transit. As AI features creep deeper into private messaging, “trust architecture” is becoming a selling point, not a footnote.
Meta isn’t betting exclusively on Nvidia
Even with this scale-up, Meta has kept optionality on the table. It develops in-house silicon, has used AMD chips, and has been reported to explore other accelerators—an especially notable point after reports last year suggested Meta was looking at Google TPUs for 2027.
That pressure to diversify is only going to grow. Every hyperscaler wants leverage, supply stability, and a second source. But this deal underscores an uncomfortable reality for Nvidia’s rivals: when the job is “build the biggest AI clusters on Earth,” most roads still run through Nvidia.
Co-design, frontier models, and the payoff Meta is chasing
Engineering teams from Nvidia and Meta will work together in “deep co-design” to optimize Meta’s AI systems and accelerate model development. That matters because Meta is trying to turn infrastructure into product velocity—shipping faster, training bigger, and pushing new AI features across Facebook, Instagram, and WhatsApp without reliability regressions.
Meta is also reportedly developing a new frontier model codenamed Avocado as a successor to Llama. Whether that becomes a breakout hit will depend on more than raw compute—but compute is the prerequisite. And with this Nvidia partnership, Meta is making it clear it intends to have that prerequisite covered for years.