GoKawiilNvidia announced more details about its new 88-core Vera data center CPUs at GTC 2026 here in San Jose, California, claiming impressive 50% performance gains over standard CPUs, fueled by a 1.5X increase in IPC from its Olympus cores and an innovative high-bandwidth design that Nvidia says delivers the fastest single-threaded performance on the market. The company also unveiled its new Vera CPU Rack architecture, which brings 256 liquid-cooled CPUs into one rack for CPU-centric workloads, claiming a 6X gain in CPU throughput and twice the performance in agentic AI workloads.
The evolution of the Vera CPU and its integration into deployable rack-scale systems marks Nvidia’s entry into direct CPU sales, positioning itself as a competitor to Intel and AMD in the traditional CPU market. That’s not to mention competing against the many flavors of custom Arm processors used by the world’s largest hyperscalers. This doesn’t come as a complete surprise, coming in the wake of the company’s announcement that Meta will now deploy multiple generations of Nvidia CPU-only systems across its infrastructure. Nvidia will also continue to use the CPUs for its own GPU-focused systems, such as the Vera Rubin platform we covered more in depth here.
Nvidia originally introduced its first-gen Grace CPUs at GTC in 2022 , foreshadowing that its continued evolution of the series would eventually position it to compete with the broader CPU market. The new processors target both AI-centric and more general-purpose use-cases, with a heavy emphasis on the former, and Nvidia’s broadening of both the capabilities and its target markets will provide stiff competition for AMD and Intel as they battle for sockets in AI data centers. The chips are now in full production and will be available to Nvidia’s partners in the second half of this year. Let’s take a closer look at the new chips, and then the rack-scale architecture.
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Nvidia Vera CPU specifications and performance
Nvidia designed the Vera CPU to provide the best of many worlds, with the intention of melding the high core counts of hyperscale cloud CPUs with the high single-thread performance of gaming CPUs and the power efficiency of mobile chips, all with the goal of speeding common GPU-driven tasks in agentic AI, training, and inference workloads, such as Python execution, SQL queries, and code compilation.
All told, Nvidia claims 1.5x the performance-per-sandbox over x86 competitors, 3x the memory bandwidth per core, and twice the efficiency. To meet those goals, the company designed an 88-core CPU with 176 threads, an increase over the first-gen Grace’s 72 cores. Nvidia also claims the cores offer a 1.5X improvement in instructions per cycle (IPC) throughput, a massive generational jump relative to other competing architectures, which tend to gain a single-digit or a low-teens percentage increase with each generation. With the previous-gen Grace, Nvidia used off-the-shelf Arm Neoverse cores, but the firm does stipulate that the new Olympus cores found on Vera are ‘Nvidia designed,’ signaling that the company has made custom modifications to the reference design.
The Arm v9.2-A Olympus cores feature spatial multi-threading, which physically isolates the various components of the pipeline by not time-slicing the key elements, like the execution units, caches and register files, with the other thread running on the same core. This contrasts with the standard time-slicing found in other simultaneous multi-threading (SMT) implementations, a process that has the threads take turns utilizing the resources. Spatial Multi-Threading increases Instruction Level Parallelism (ILP), throughput, and performance predictability by pulling instructions from other threads when execution elements are idle, thus ensuring full utilization.
In effect, this allows both threads to truly run simultaneously on a single core, whereas in a standard SMT implementation the threads essentially take turns running on a single core. Naturally, this will be a boon for multi-tenancy environments.
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