For years, the data-center CPU race has revolved around a fairly straightforward question: how many cores can fit into a server, and how much work can they chew through at once? NVIDIA now argues that this pursuit of scale came with a trade-off that matters more as AI systems become increasingly autonomous.
In an interview with Wccftech, the company’s Vera team said the industry’s shift toward chiplet-based designs and higher core counts came at the expense of single-threaded performance and instructions per cycle (IPC). With AI agents demanding faster execution between successive reasoning steps, NVIDIA believes the CPU needs a rethink.
As cloud data centers grew, chipmakers focused on squeezing more cores into servers to handle more workloads at once. Chiplet designs helped make this possible by splitting a processor across multiple smaller dies instead of building everything on a single, larger die. But NVIDIA’s Vera team argues that this approach comes with trade-offs. Moving data between dies can add communication delays and complicate memory access, potentially limiting how much single-threaded performance each core can deliver. That doesn’t make chiplets a bad design choice, but NVIDIA believes the industry’s push for more cores has come at the expense of faster individual cores.
“…what suffered over that time in terms of CPU design was really single-threaded perf and IPC because when you fill, when you use chiplets, you just, you literally don’t have the space on the, on the chip to design for per core perf.”
Agentic AI is changing that equation. Unlike a chatbot that simply generates a response, an AI agent may need to execute code, call external tools, retrieve information, analyze the results, and then decide what to do next. While some tasks can run simultaneously, others must finish before the next step can begin. That’s where single-threaded performance, or how quickly a CPU can handle work on one thread, becomes important. NVIDIA argues that faster execution and lower latency can help agents move through these steps more quickly.
There’s also a knock-on effect for GPUs. If a CPU takes too long to prepare the next task or process an agent’s output, the GPU may sit idle while waiting for instructions, wasting valuable computing capacity. Simply adding more CPU cores won’t necessarily solve that problem if individual tasks remain slow. NVIDIA argues that agentic AI needs a better balance between core counts and per-core speed, so the CPU can keep pace with the GPU rather than becoming the bottleneck
Vera aims to combine faster individual cores with the throughput modern data centers need. It packs 88 custom Olympus CPU cores onto a monolithic compute die, alongside a custom LPDDR5X memory subsystem. NVIDIA says its second-generation Scalable Coherency Fabric delivers up to 3.4 TB/s of bisection bandwidth, three times that of leading x86 chips. Keeping the cores on a single compute die also avoids communication across separate chiplets. These are NVIDIA’s claims, but the strategy is clear: prioritize fast, efficient communication without giving up the benefits of high core counts.
Vera also signals a shift in how NVIDIA sees CPUs fitting into AI infrastructure. While its Grace CPU primarily serves as a host for GPU-powered systems, NVIDIA says agentic AI is creating demand for standalone CPU racks to handle tool calls, code execution, and reinforcement learning. That doesn’t mean GPUs are becoming less important or that chiplets are going away. NVIDIA itself says it remains open to chiplet designs when they make sense.
The bigger point is that AI is changing what a good CPU looks like. As agents move beyond answering questions to executing multi-step tasks, raw core counts alone may no longer tell the whole story. The next battle isn’t just about cramming more cores into a chip. It’s about making each core fast enough to keep the AI factory running, rather than leaving its most expensive machinery waiting around.
Nearly a month after announcing the GPT-6.1 Sol model, OpenAI unveiled GPT-6.1 Terra today. The latest addition to the family focuses on computer use, visual reasoning, coding, and professional work, while costing 50% less through the API than GPT-5.6 Terra.
OpenAI says Terra can handle everything from filling out browser forms and working inside spreadsheets to interpreting technical drawings and editing videos. The company says it can also complete computer-based tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol.
ASUS has started selling its UGen Intel Arc Pro B70 graphics card in Europe, bringing its take on Intel’s workstation GPU to customers with a hefty €1,799 recommended price. The card is available in Germany and Austria, while ASUS lists it at CHF 1,799 in Switzerland.
Intel first introduced the Arc Pro B70 in March 2026, with its own branded model starting at $949. ASUS’s version arrives several months later with the same core GPU architecture but a different cooling design and a higher power rating. The price also places it well above Intel’s original starting point, although the two prices are not directly comparable because ASUS’s European figure includes VAT.
Anthropic has officially filed to go public, and buried inside its S-1 is a blunt warning about the technology at the center of its business. The maker of Claude AI says advanced AI could cause everything from large-scale cyberattacks and geopolitical instability to catastrophic or even existential risks for humanity.
The warnings take up a surprisingly large part of the filing. Axios notes that more than 90 pages, or roughly a third of the prospectus, focus on risk factors. Many of them warn about what could happen as AI systems become more capable and autonomous.