- Meta and Nvidia launch multi-year partnership for hyperscale AI infrastructure
- Millions of Nvidia’s Arm-based GPUs and CPUs will be able to handle extreme workloads
- Unified architecture covers data centers and Nvidia cloud partner deployments
Meta announced a multi-year partnership with Nvidia to create hyperscale AI infrastructure capable of handling some of the technology industry’s largest workloads.
This collaboration will deploy millions of Arm GPUs and processors, expand network capacity, and integrate advanced privacy-preserving computing techniques across the company’s platforms.
The initiative aims to combine Meta’s extensive production workloads with Nvidia’s hardware and software ecosystem to optimize performance and efficiency.
Unified architecture across all data centers
The two companies are creating a unified infrastructure architecture that spans on-premises data centers and Nvidia cloud partner deployments.
This approach simplifies operations while providing scalable, high-performance computing resources for AI training and inference.
“No one is deploying AI at the scale of Meta – integrating cutting-edge 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 of Nvidia.
“Through in-depth design of CPUs, GPUs, networking and software, we bring the complete Nvidia platform to Meta researchers and engineers as they lay the foundation for the next frontier of AI. »
Nvidia’s GB300-based systems will form the backbone of these deployments. They will offer a platform integrating compute, memory and storage to meet the demands of next-generation AI models.
Meta also extends the Nvidia Spectrum-X Ethernet network across its entire footprint and aims to deliver predictable, low-latency performance while improving operational and energy efficiency for large-scale workloads.
Meta has begun adopting Nvidia Confidential Computing to support AI-based capabilities within WhatsApp, enabling machine learning models to process user data while maintaining privacy and integrity.
The collaboration plans to extend this approach to other Meta services, integrating privacy-enhanced AI techniques across multiple applications.
The Meta and Nvidia engineering teams work closely to co-design AI models and optimize software across the entire infrastructure stack.
By aligning hardware, software and workloads, companies aim to improve performance per watt and accelerate training for cutting-edge models.
The large-scale deployment of Nvidia Grace processors is at the heart of this effort, with the collaboration representing the first major Grace-only deployment at this scale.
Software optimizations in CPU ecosystem libraries are also implemented to improve throughput and power efficiency of successive generations of AI workloads.
“We are excited to expand our partnership with Nvidia to create cutting-edge clusters using their Vera Rubin platform to deliver personal superintelligence to everyone around the world,” said Mark Zuckerberg, Founder and CEO of Meta.
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