Meta Builds AI Infrastructure with Nvidia
Meta's AI Roadmap Supported by Large-Scale Deployment of Nvidia CPUs, Networking and Millions of Nvidia Blackwell and Rubin GPUs
This is a Press Release edited by StorageNewsletter.com on February 27, 2026 at 2:01 pmSummary:
- Meta expands Nvidia CPU deployment and significantly improves performance per watt in its data centers
- Meta scales out AI workloads with Nvidia Spectrum-X Ethernet, supporting network efficiency and throughput
- Meta has adopted Nvidia Confidential Computing, enabling AI capabilities while protecting user privacy
Nvidia Corp. announced a multiyear, multigenerational strategic partnership with Meta spanning on-premises, cloud and AI infrastructure.
Meta will build hyperscale data centers optimized for both training and inference in support of the company’s long-term AI infrastructure roadmap. This partnership will enable the large-scale deployment of Nvidia CPUs and millions of Nvidia Blackwell and Rubin GPUs, as well as the integration of Nvidia Spectrum-X Ethernet switches for Meta’s Facebook Open Switching System platform.
“No one deploys AI at Meta’s scale – integrating frontier research with industrial-scale infrastructure to power the world’s largest personalization and recommendation systems for billions of users,” said Jensen Huang, founder and CEO, Nvidia. “Through deep codesign across CPUs, GPUs, networking and software, we are bringing the full Nvidia platform to Meta’s researchers and engineers as they build the foundation for the next AI frontier.”
“We’re excited to expand our partnership with Nvidia to build leading-edge clusters using their Vera Rubin platform to deliver personal superintelligence to everyone in the world,” said Mark Zuckerberg, founder and CEO, Meta.
Expanded Nvidia CPU deployment for performance boost
Meta and Nvidia are continuing to partner on deploying Arm-based Nvidia Grace CPUs for Meta’s data center production applications, delivering significant performance-per-watt improvements in its data centers as part of Meta’s long-term infrastructure strategy.
The collaboration represents the first large-scale Nvidia Grace-only deployment, supported by codesign and software optimization investments in CPU ecosystem libraries to improve performance per watt with every generation.
The companies are also collaborating on deploying Nvidia Vera CPUs, with the potential for large-scale deployment in 2027, further extending Meta’s energy-efficient AI compute footprint and advancing the broader Arm software ecosystem.
Unified architecture supports Meta’s AI infrastructure
Meta will deploy industry-leading Nvidia GB300-based systems and create a unified architecture that spans on-premises data centers and Nvidia Cloud Partner deployments to simplify operations while maximizing performance and scalability.
In addition, Meta has adopted the Nvidia Spectrum-X Ethernet networking platform across its infrastructure footprint to provide AI-scale networking, delivering predictable, low-latency performance while maximizing utilization and improving both operational and power efficiency.
Confidential computing for WhatsApp
Meta has adopted Nvidia Confidential Computing for WhatsApp private processing, enabling AI-powered capabilities across the messaging platform while ensuring user data confidentiality and integrity.
Nvidia and Meta are collaborating to expand Nvidia Confidential Compute capabilities beyond WhatsApp to emerging use cases across Meta’s portfolio, supporting privacy-enhanced AI at scale.
Codesigning Meta’s next-generation AI models
Engineering teams across Nvidia and Meta are engaged in deep codesign to optimize and accelerate state-of-the-art AI models across Meta’s core workloads. These efforts combine Nvidia’s full-stack platform with Meta’s large-scale production workloads to drive higher performance and efficiency for new AI capabilities used by billions around the world.
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Meta has reaffirmed its ambition to rank among the leaders in artificial intelligence, competing with top hyperscalers and emerging AI-native companies. It is part of a select group capable of conducting large-scale model training, going well beyond traditional inference-focused approaches.
For Mark Zuckerberg, this push is also personal—particularly following the setbacks surrounding Llama 4, which served as a significant wake-up call. The strategy reinforces Meta’s dual-track approach: investing both in open-source initiatives and in its pursuit of advanced, potentially superintelligent systems.
At the same time, open source plays a strategic role as a powerful recruitment channel, helping attract top talent from research institutions and universities.
Clear strategic divides are emerging across the AI landscape: open-source versus proprietary models, and consumer-focused offerings versus enterprise solutions. These distinctions highlight how critical business models and go-to-market strategies have become in shaping competitive positioning.
To sustain its ambitions, the company requires massive computing power - GPUs, alternative accelerators, and next-generation CPUs. A few months ago, the Palo Alto–based giant reportedly attempted to acquire the fast-growing Korean GPU startup FuriosaAI for $800 million. Founded in 2017, the company has raised approximately $240 million across six funding rounds to date.
As seen a few months earlier with OpenAI - first announcing news with Nvidia and shortly after with AMD - Meta is now following a similar pattern. It revealed a partnership with Nvidia on February 17, followed by another announcement with AMD on February 23.
This multi-sourcing approach is a pragmatic response to the ongoing shortage of advanced computing components. The intense competition for high-performance processors has created a clear race among major players, helping explain why some companies are investing heavily in designing and developing their own custom silicon solutions.
The agreement is particularly significant, spanning multiple generations of processors and accelerators - including Grace and Rubin CPUs, as well as Blackwell and Rubin GPUs - alongside high-speed networking technologies. It reflects a multi-year, multi-generation commitment, at a time when Nvidia continues to roll out new products at an accelerated pace under intense market pressure.
The scope of the deal extends across on-premises systems, cloud environments, and broader AI infrastructure, signaling a major refresh cycle ahead. At the same time, Meta remains highly focused on energy efficiency and optimization, which strongly influences its choice of locations for building new data centers.
The company has multiplied its AI initiatives in recent years. It established a dedicated AI lab led by Yann LeCun, who is now focusing on his own effort, Advanced Machine Intelligence Labs.
Last year, Meta acquired key assets from Scale AI in a $14 billion deal, and Alexandr Wang, the company’s CEO, became Chief AI Officer, now heading Meta’s Superintelligence Labs. These moves have reportedly generated internal tensions, underscoring how central - and strategically sensitive - AI has become within Meta.
More recently, several key members of Thinking Machines Lab - the company founded in 2025 by Mira Murati, former CTO of OpenAI - have joined Meta, further strengthening its AI talent base.
As Hammerspace and Pure Storage actively highlight their deployments at Meta, it will be interesting to see how their technologies integrate into this new strategic phase.
More broadly, Meta is increasingly viewed as one of the United States’ key assets in maintaining AI leadership amid intensifying global competition. Its trajectory could play a significant role in shaping technological dominance and national competitiveness in the years ahead.
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