Nvidia is no longer content with owning the GPU crown. With a newly expanded multiyear data center agreement with Meta that includes large-scale deployment of its Grace CPU-only servers, the AI chip leader is stepping deeper into territory long controlled by Intel and increasingly contested by AMD. While GPUs remain the backbone of advanced AI training, Nvidia’s strategic CPU expansion signals a broader ambition: capturing more of the total value inside hyperscale data centers and potentially disrupting the traditional x86 stronghold.

The implications extend beyond hardware. This move reshapes competitive dynamics in one of the most capital-intensive segments of the semiconductor industry.

From GPU Leader to Full-Stack Data Center Powerhouse

Nvidia’s agreement with Meta includes millions of Blackwell and Rubin GPUs, but the more structurally significant detail is Meta’s adoption of Nvidia’s Grace CPU-only servers. Grace is typically paired with Blackwell GPUs in Nvidia’s GB200 and GB300 AI superchips. Deploying it independently marks the first large-scale validation of Nvidia as a standalone CPU supplier.

The strategic logic is clear. Hyperscalers are increasingly using CPUs for AI inferencing and agentic workloads that do not require the extreme parallel processing power of GPUs. By supplying both compute layers, Nvidia can increase its share of wallet per data center build.

Since acquiring Mellanox in 2020, Nvidia has expanded into networking, meaning it now touches compute, acceleration, and connectivity. Adding CPUs further deepens vertical integration. Instead of selling components, Nvidia is positioning itself as a full-stack infrastructure provider.

Pressure Mounts on Intel and AMD

Intel has historically dominated data center CPUs, while AMD has steadily gained share with its EPYC processors. Nvidia’s entry arrives at a vulnerable moment. Intel is grappling with capacity constraints, limiting its ability to meet demand. Analysts have pointed out that Intel’s recent equipment divestitures now appear mistimed as AI-driven data center expansion accelerates.

AMD, while operationally stronger than Intel in recent years, also faces competitive risk if hyperscalers diversify away from x86 architecture. Nvidia’s Grace chips, like Amazon’s Graviton, Google’s Axion, and Microsoft’s Cobalt, are based on Arm architecture. A broader migration toward Arm could structurally weaken the x86 ecosystem.

Still, the competitive landscape is nuanced. Nvidia continues to collaborate with Intel in certain server configurations combining Nvidia GPUs with Intel CPUs. The rivalry does not preclude tactical partnerships.

Beyond Data Centers: The Consumer Angle

Nvidia’s CPU ambitions may also extend into consumer laptops. Reports suggest upcoming N1 and N1X processors could power Lenovo devices. While the consumer CPU market is smaller than Nvidia’s data center business—which generated $51.2 billion in a single quarter—it represents strategic diversification.

For gamers and AI-powered PC applications, vertically integrated Nvidia systems could offer performance optimization advantages. However, margins and scale in consumer CPUs differ materially from hyperscale deployments.

Looking ahead, Nvidia’s CPU strategy is less about replacing GPUs and more about eliminating bottlenecks in the AI supply chain. By controlling more layers of compute, Nvidia reduces dependency on competitors and strengthens pricing power.

For investors, the key question is whether hyperscalers will meaningfully shift CPU procurement toward Nvidia or continue diversifying among in-house and third-party designs. If adoption accelerates, Intel and AMD may face sustained competitive compression. If not, Nvidia’s move could remain a complementary expansion rather than a structural takeover.

Either way, Nvidia’s evolution from accelerator specialist to infrastructure architect underscores a larger semiconductor reality: in the AI era, dominance requires owning more than just one chip.