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Nvidia has shipped 'hundreds of thousands of Grace standalone servers’ — GPU firm pivots messaging as CPUs take center stage in agentic data centers

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Why This Matters

Nvidia's shipment of hundreds of thousands of Grace standalone servers highlights its strategic pivot towards CPU dominance in data centers, especially as workloads shift from GPU-centric AI inference to CPU-based data processing. This move signifies Nvidia’s effort to compete more directly with established CPU giants like Intel and AMD, emphasizing the evolving hardware landscape driven by agentic AI workloads. For consumers and the industry, this underscores a broader trend of integrated hardware solutions tailored for data-intensive tasks, potentially reshaping data center infrastructure.

Key Takeaways

Nvidia’s Ian Buck, vice president of hyperscale and high-performance computing and the inventor of CUDA, says the company has “shipped... let's put it in the hundreds of thousands of Grace standalone servers.” In May, Nvidia disclosed that it had shipped over 2.5 million Grace CPUs in total, and the company announced a partnership with Meta to deploy standalone Grace servers in February. Buck’s comments suggest the scale of deployment may be even larger, however, as Nvidia tries to compete in a market dominated by other players.

It’s an interesting comment, though not a surprising one. Nvidia has become the dominating force of Silicon Valley as demand for its GPUs skyrocketed during an unprecedented data center buildout for AI inference. Since peaking earlier this year, however, around $1 trillion in Nvidia’s market cap has been wiped away as investors rally behind CPU makers like Intel . Evolving agentic AI workloads have changed the hardware balance, shifting away from as many as eight GPUs per CPU, toward a one-to-one ratio in some cases.

Nvidia wants to ride that train with its new Vera CPU, which was architected specifically for those types of workloads. Even before the recent rise of agents, however, Nvidia says it has seen demand for its CPUs for data-hungry workloads. “They weren’t running a web server [with Grace]… or they aren’t being used for, what the cloud uses, of cheap, dollar-per-core,” Buck said. “They were being deployed for the backend, data-rich operations, like the data processing.”

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Grace represents an on-ramp for Nvidia into data center CPUs. It uses 72 stock Arm Neoverse V2 cores, but it’s differentiated by Nvidia’s Scalable Coherency Fabric (SCF). Vera uses an updated SCF, but it also features Nvidia’s first custom core design, called Olympus. Grace cracked the door, and Vera represents Nvidia's big entrance into the market against AMD and Intel.

Regardless of where Vera ends up in the battle of next-gen data center CPUs — which is heating up now, as AMD is expected to launch its Zen 6 Venice CPUs this week — the design is vastly different from what we’ve seen out of Intel and AMD. Most notably, Vera is monolithic, placing all of its 88 cores on a single piece of silicon. AMD and Intel, years ago at this point, pivoted away from monolithic dies in favor of chiplets, allowing an extremely high density of cores at the cost of latency and coherency issues. Vera is radically different in that regard, not only being built on a single die, but also dedicating significant die space to the fabric.

“One of the reasons we don’t have 128 cores is because we’ve dedicated so much of the die area toward the fabric,” Buck said. “It’s 3.4 TB/s of bandwidth inside of that CPU that is dedicated toward allowing every core to talk to every cache, every memory [controller] at full speed without any collisions.”

For clarification’s sake, Buck is referencing 3.4 TB/s of core-to-core bandwidth in Vera. There’s up to 1.2 TB/s of aggregate memory bandwidth (14 GB/s per core) through the LPDDR5X interface.

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But just as chiplet-based designs made trade-offs in per-thread performance, Vera will likely make trade-offs for its unique architecture. The majority of data center workloads are still “legacy” tasks that hyperscalers have built for, and even with seemingly insatiable demand for AI infrastructure, that is unlikely to change for several years.

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