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Hot Chips 2026: Nvidia breaks down 88-core Vera CPU — spatial multithreading benchmarked, 1.2 TB/s SOCAMM2 memory, agentic workloads detailed, and more
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Nvidia has spent the last several months providing key disclosures about its next-gen Vera CPU for agentic data centers, which it continued at Hot Chips 2026. Although we've already learned a lot about Vera, how it compares to AMD's next-gen Venice CPUs, and the inner workings of the Olympus core, Nvidia provided a bit more color at Hot Chips on spatial multithreading, the memory subsystem, and what types of workloads it's targeting with Vera.
As a quick refresher, Vera is the first CPU with a custom Nvidia core, following up on Grace, which used a stock Arm design. It's shipping as a single, 88-core SKU, and it has some key design differences compared to Nvidia's x86 competition, most notably a multi-threading implementation that Nvidia calls spatial multi-threading, an LPDDR5X memory subsystem, and a monolithic compute die rather than using compute chiplets.
Nvidia says it's designed Vera specifically for agentic AI workloads, a category that's still being defined in terms of performance benchmarking. Many CPU-intensive tasks serve as proxies for agentic workloads (i.e., code compilation), though measuring performance across a full agentic chain is complex and inconsistent. Nvidia, in its own slides (see the end of this article), calls agentic AI the "most complex computing workload in history," after all.
Nvidia provided an example of a headless browser to show the benefits of Vera, using optimized code to mimic how an agent would use a browser. Compared to the 96-core EPYC 9655P, Nvidia says Vera runs 24% faster as browser instances scale.
This slide is a good demonstration of the complexities in measuring traditional workloads and applying that performance to agentic workflows. Agents will often fetch websites for information, but there are several layers where agents can trim back compared to humans; in this case, agents can run through a browsing workflow 4.5x faster by cutting things like GUI rendering, fonts, media decoding, and more.
Another touchstone for agentic performance is code compilation, as agents seek out software to compile on the system. This might be the most direct benchmark of agentic AI performance with current workflows right now. Though, as previously mentioned, agentic chains are long, complex, and involve several different workloads.
Once again, compared to the 96-core EPYC 9655P, Nvidia claims Vera can compile the Linux kernel 22% faster with a native AArch64 target, and 14% faster when cross-compiling for x86.
Nvidia reiterated the importance of the large cores inside Vera, including the large BPU, neural branch predictor, and 10-wide decode. Nvidia has previously disclosed the Olympus core architecture, which you can read about in our Vera deep dive. Broadly speaking, however, it's a wide core optimized for high single-core throughput.