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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →CoreWeave’s NVIDIA V100 GPUs are still running customer workloads nearly a decade after Volta launched, according to NVIDIA executive Ian Buck. At the other end of the timeline, CoreWeave says its cloud has made NVIDIA Vera Rubin NVL72 available, with Cognition as the first customer running production workloads on the system. Cognition reported up to 4.8x higher total token throughput on one specific inference workload—not a general 4.8x speedup for every AI task.
What Ian Buck said about CoreWeave’s V100 GPUs
In a September 30, 2026 article, NVIDIA quoted Ian Buck, its vice president of hyperscale and high-performance computing: “CoreWeave’s NVIDIA V100 GPUs are still running customer workloads nearly a decade after Volta launched, even as CoreWeave brings Vera Rubin NVL72 into production.” NVIDIA’s announcement presents this as a statement about CoreWeave’s deployed fleet. It is not an independent audit of the cards’ age or condition, nor evidence that every V100 lasts that long or remains economical for every workload.
The announcement contrasts continued use of a Volta-generation accelerator with a new rack-scale system entering production. That establishes ongoing service and a new deployment, not comparable performance, operating cost, power efficiency, or suitability. The age of hardware alone cannot answer whether it is the right choice for a particular workload.
What the reported 4.8x figure measures
The number refers to up to 4.8x higher total token throughput for Cognition’s SWE-2 inference workload on Vera Rubin NVL72 compared with a GB200 NVL72 baseline. NVIDIA describes the result as coming from Cognition’s early tests; CoreWeave’s September 30, 2026 announcement also describes the benchmark as run on CoreWeave Cloud.
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| Comparison element | What the announcement reports |
|---|---|
| New system | Vera Rubin NVL72 |
| Baseline | GB200 NVL72 |
| Workload | Cognition’s SWE-2 inference |
| Metric | Total token throughput |
| Reported result | Up to 4.8x higher throughput in early tests |
Throughput is not the same as a promise that each token, prompt, or response will be generated 4.8 times faster. “Up to” describes the reported result’s ceiling, and the comparison is tied to this workload and system baseline. NVIDIA says Cognition sampled a subset of tasks from FrontierCode and deployed AI agents to solve them. The published announcements do not include a full configuration or reproducible test report, confidence intervals, or broad results across models and workloads, so the figure should not be generalized beyond the stated comparison.
Why Cognition’s workload matters—and what it does not show
NVIDIA says Cognition uses CoreWeave for training, reinforcement learning, and production inference for Devin, and scaled to thousands of GPUs on CoreWeave in nine months. This context helps explain why a customer would care about inference throughput, but the 4.8x result applies specifically to SWE-2 inference; it is not a result for all of Cognition’s work.
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CoreWeave separately reports a 3.8x boost in output token throughput for reinforcement-learning workloads. That is a distinct workload-specific figure, not another way to state the 4.8x SWE-2 result. Both announcements come from the companies involved, and the available reporting does not supply an independent study to validate or extend either comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Vera Rubin NVL72 going live means for CoreWeave customers
NVIDIA describes Vera Rubin NVL72 as a rack-scale system and says CoreWeave announced its availability on CoreWeave Cloud. Cognition is identified as the first customer running production workloads on it. This is a cloud availability and production-use announcement; it does not establish that every customer can access the system immediately or that it is available in every region or configuration.
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The contrast is therefore about two different points in the hardware lifecycle: V100 accelerators still serving CoreWeave customer workloads, as NVIDIA describes them, and Vera Rubin NVL72 newly entering production on the cloud. The reported benchmark offers a narrow performance comparison for one customer workload, not a verdict that newer hardware replaces older systems in every role.
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