Nvidia Secures Multiyear Meta Deal, Expands into CPU Market to Counter AI Chip Competition

Nvidia has solidified its dominance in the AI hardware ecosystem with a landmark multiyear agreement to supply Meta Platforms with millions of artificial intelligence chips, including a groundbreaking entry into the standalone CPU market. According to Reuters, the deal, announced on Tuesday, encompasses both current and future generations of Nvidia’s AI accelerators, extending the tech giant’s footprint within one of the world’s largest data center operators. This marks the first time Nvidia will provide Meta with dedicated central processing units, signaling a strategic pivot to compete more directly with established CPU makers like Intel and AMD.

Meta’s expansion of its partnership with Nvidia, as reported by CNBC, is driven by the urgent need to scale its global AI infrastructure to support next-generation generative AI models, including those powering Facebook, Instagram, WhatsApp, and its emerging metaverse initiatives. The company plans to deploy these chips across its data centers in the U.S., Europe, and Asia, with the new CPU components expected to optimize data routing, reduce latency, and improve overall system efficiency in large-scale AI training environments. The inclusion of standalone CPUs — distinct from Nvidia’s traditional GPU-centric offerings — represents a significant departure from its historical focus and a direct response to growing pressure from competitors developing integrated AI systems.

The move comes amid intensifying competition in the AI silicon space. Companies such as AMD, Intel, and even Meta’s own in-house chip development team, Meta AI, are accelerating efforts to offer alternatives to Nvidia’s Hopper and Blackwell architectures. According to MSN, Meta’s deepening alliance with Nvidia is not merely a procurement decision but a calculated effort to lock in supply and co-develop custom silicon solutions that align with its unique AI workloads. Sources indicate that the partnership includes joint R&D initiatives focused on optimizing CPU-GPU communication protocols and developing specialized memory architectures to handle the massive data throughput required by trillion-parameter models.

Nvidia’s entry into the standalone CPU market, while initially limited to Meta, could set a precedent for future deals with other hyperscalers. Analysts suggest this move is a defensive strategy to prevent cloud providers from developing proprietary alternatives that could erode Nvidia’s near-monopoly on AI training hardware. The company’s existing dominance in GPU-based AI acceleration has been challenged by the rise of specialized AI chips from startups and the growing interest in open-source AI frameworks that favor more modular hardware stacks.

For Meta, the deal ensures supply chain stability amid global semiconductor shortages and geopolitical tensions affecting chip manufacturing. The multiyear nature of the agreement provides budget predictability and enables long-term planning for its AI roadmap. Internal documents reviewed by industry analysts suggest Meta aims to double its AI training capacity by 2028, a goal heavily dependent on the performance and scalability of the chips supplied by Nvidia.

While financial terms of the deal remain confidential, industry estimates suggest the contract could exceed $10 billion over its lifespan. Nvidia’s stock rose nearly 4% in after-hours trading following the announcement, reflecting investor confidence in its ability to extend its market leadership beyond GPUs. Meanwhile, Meta’s shares gained modestly, as markets interpreted the deal as a sign of continued commitment to AI innovation despite recent scrutiny over platform content and user growth.

As the AI race heats up, this partnership exemplifies the evolving nature of tech alliances — where traditional supplier-customer relationships are becoming co-innovation ecosystems. Nvidia is no longer just a chip vendor; it is now a strategic infrastructure partner. And Meta, once a pure software platform, is becoming a silicon architect in its own right — one that now demands control over every layer of its AI stack.