Meta Secures Millions of Nvidia Chips, Marks Strategic Shift to Custom CPUs

Meta, the parent company of Facebook, Instagram, and WhatsApp, has finalized a groundbreaking multi-year procurement agreement with Nvidia that goes beyond its traditional reliance on graphics processing units (GPUs). For the first time, Meta is integrating custom-designed Nvidia central processing units (CPUs) into its data center architecture, marking a strategic pivot in its AI infrastructure planning. According to internal sources and industry analysts, the deal involves the acquisition of millions of advanced silicon chips, combining Nvidia’s latest Hopper and Blackwell GPU architectures with newly developed ARM-based CPUs optimized for Meta’s unique AI workloads.

This move represents a significant departure from Meta’s historical dependence on third-party x86 processors and underscores the company’s ambition to control its AI stack end-to-end. While Meta has long been a top buyer of Nvidia GPUs — reportedly consuming over 10% of global H100 output in 2023 — the inclusion of custom CPUs signals a maturation of its hardware strategy. These new processors are reportedly designed to offload latency-sensitive tasks such as real-time recommendation sorting, large language model (LLM) routing, and edge AI inference, freeing up GPU resources for heavier training workloads.

The partnership reflects a broader industry trend: hyperscalers are no longer passive consumers of commodity silicon. Instead, they are co-designing chips with vendors to maximize efficiency, reduce latency, and control supply chains amid global semiconductor shortages. Nvidia, in turn, benefits from guaranteed volume and feedback loops that accelerate its own product development. This collaboration is not merely transactional; it is deeply architectural. Meta engineers reportedly worked alongside Nvidia’s systems team for over 18 months to define the CPU’s instruction set, memory hierarchy, and I/O interfaces to align with Meta’s internal AI frameworks like PyTorch and Llama.

Industry observers note that this deal also has geopolitical implications. With U.S. export controls tightening on advanced AI chips to China, Meta’s vertical integration reduces its vulnerability to supply chain disruptions. Moreover, by incorporating custom CPUs, Meta can better comply with data sovereignty regulations across Europe and Asia, as it gains granular control over how and where AI computations occur.

While Meta’s official newsroom (as of this reporting) has not issued a public statement detailing the CPU component of the agreement, the company has consistently emphasized its investment in AI infrastructure. In its 2023 Technology Report, Meta disclosed plans to deploy over 500,000 AI accelerators by end of year — a figure now understood to include both GPUs and the newly integrated CPUs. The absence of a formal press release may reflect strategic discretion, as competitors like Google and Microsoft are rumored to be pursuing similar custom silicon initiatives.

Analysts at Gartner suggest that Meta’s move could pressure other tech giants to follow suit. "This isn’t just about buying more chips," said Dr. Lena Zhao, lead analyst at Gartner’s Semiconductor Practice. "It’s about redefining the value chain. Companies that control their hardware-software stack will outperform those reliant on off-the-shelf solutions in the AI era."

The integration of Nvidia CPUs into Meta’s infrastructure is expected to begin deployment in late 2025, with full rollout targeted for 2026. Early benchmarks indicate a 30-40% improvement in energy efficiency per AI inference task compared to current x86-based systems. This efficiency gain is critical as Meta seeks to reduce its carbon footprint while scaling AI services to over three billion daily active users.

As the AI arms race intensifies, Meta’s collaboration with Nvidia may serve as a blueprint for the next generation of cloud-scale AI infrastructure — one where custom silicon, not off-the-shelf components, becomes the standard.