Standalone Nvidia Vera CPUs land in Oracle Cloud's multi-gigawatt deployment

Nvidia and Oracle have announced general availability of Nvidia’s Vera Rubin NVL72 rack and Vera CPUs on Oracle Cloud Infrastructure (OCI) Superclusters at Oracle AI World on Tuesday. The announcement comes hot on the heels of Google Cloud announcing general availability for Vera Rubin last week, putting two major hyperscaler feathers in Nvidia’s cap. However, Oracle’s announcement is interesting, specifically calling out standalone Vera instances on OCI Superclusters. We've included a few benchmarks we've performed on a Vera server for a forthcoming review to highlight some of the chip's performance characteristics, but do note that these tests were not conducted on OCI infrastructure.

Neoclouds have been banging the drum of Vera for the past several weeks, with instances under way at Nscale, CoreWeave, and Nebius, among others, so it makes sense to see Vera in the GPU-dominant and AI-focused neoclouds. A standalone deployment at a hyperscaler like Oracle sends a different signal, however.

Although hyperscalers maintain large fleets of hardware, sometimes even renting from the neoclouds, the CPU focus is primarily on x86, with OCI Superclusters otherwise offering the Ampere Altra for an Arm-based instance. It’s Nvidia’s first standalone CPU push into the hyperscale data center, placing Vera alongside the x86 competition regardless of the GPU compute.

Vera is not meant to upend the x86 guard, at least in isolation. Ian Buck, Nvidia’s mind behind CUDA and the face of Nvidia’s AI infrastructure buildout, said in March — when the chip was revealed — that the world couldn’t be served by a single, 88-core SKU. However, Vera is opening the door at hyperscalers like Oracle to expand in future CPU generations.

AMD and Intel maintain the lead when it comes to single-socket density, which is only set to expand in the next generation, as AMD’s upcoming EPYC Venice CPUs can pack up to 512 threads in a single socket. However, Vera offers some distinct advantages due to its topology depending on the workload, many of which are becoming increasingly relevant in the agentic AI era.

Memory bandwidth, for instance, is one of Vera’s strong suits, and that’s not just a marketing claim. Nvidia has said the memory system is capable of 1.2 TB/s of bandwidth, but we can see Nvidia’s lead outside of theoretical numbers in our testing below.

Take the industry-standard Stream benchmark as an example. Nvidia’s use of LPDDR5X-9600, along with a monolithic die and distributed mesh for caching, allows it to more than double the bandwidth available on a dual Intel Xeon 6980P system, even when it’s paired with MRDIMMs sporting speeds of 8,800 MT/s. We’re showing triad here, as it’s the most “demanding” of Stream’s operations, but the leads for Vera are similar across add, copy, and scale as well.

This bandwidth is critical for some types of agentic loops, particularly those dealing with large database queries or extensive documentation. You can see the OpenMP “spread” policy offers a massive boost to Vera’s memory bandwidth, as well. Both AMD and Intel maintain centralized memory control and system-level cache, while Vera distributes it in a mesh across the die. The result, at least in OpenMP set properly, is much higher memory bandwidth by binding processes as far away from each other as possible.

Another critical performance vector for agentic AI is Python. The agentic world is expanding, but much of the foundation of the AI software ecosystem is built on Python. Common agentic frameworks like CrewAI and LangGraph are written in Python, and although we’re seeing more frameworks built in languages like Rust and TypeScript, Python performance is still critical to agentic loops.

And, unsurprisingly, it’s an area where Vera is also strong, beating out CPUs that are much more powerful on paper. Popular agentic harnesses are built on Python, but so are many of the applications that also run on Python, including foundational AI libraries like PyTorch and TensorFlow.

Nvidia’s focus on agentic AI makes sense given the times we’re living in, but it can miss the forest for the trees. Oracle bringing Vera standalone instances online shifts the focus onto the broader implications of Vera’s entrance into the data center. There are some aspects of performance that characterize agentic AI workloads, but many agent workloads are regular data center workloads in the first place, just under high concurrency.

For instance, SQLite performance is important for any server querying a large database, regardless of its written by logic, a person, or an AI agent. Nvidia’s second-gen Scalable Coherency Fabric (SCF), which we saw at play in Stream, shows up in a real workload here, with these database queries largely bound by data movement across the system.

And that is what makes Vera so interesting in the context of Oracle’s announcement. It’s a CPU built for agentic AI, says Nvidia, but the crosstalk with general data center workloads is strong. It’s not just a CPU to use because you need access to Nvidia’s accelerators. Depending on the workload, it may even be a better choice than the x86 guard.

Ultimately, we have to see how customers of these hyperscalers use (or don’t use) Vera in production. It’s demonstrated its usefulness in agentic AI loops, but it’s also showing early promise in certain general-purpose workloads. Oracle’s standalone deployment is a sign that there’s some demand for Vera that isn't exclusively tied at the hip of a Rubin GPU.

Oracle is approaching cautiously, however, as any hyperscaler should be when bringing up a multi-gigawatt deployment. The company said it will also offer Nvidia’s HGX NVL8, bringing eight Rubin GPUs together in a cluster, connected to a dual, 64-core Intel Xeon 6 host with 4TB of DDR5 memory.

Our testing of Vera shared in this article is part of a larger benchmarking session we had with the chip, and we'll share the technical details of the test setup in our forthcoming review. We’ll be publishing our full results shortly on Tom’s Hardware.