$42 Million of Agentic Work per DGX Station per Year

The risk of AI agents operating outside their defined boundaries is a key concern as AI moves from experimentation to production. Aible automatically observes and then translates such autonomous agents into locked-down Minimum Viable Agency (MVA) agents that industrialize the process to deliver business impact consistently at significantly lower cost and typically higher accuracy. To learn more about Aible MVA and the details of the cost benchmark conducted in collaboration with NVIDIA, please see How NVIDIA Nemotron 3.5 Super VL enables Aible Minimum Viable Agency.



For the recently released NVIDIA Nemotron 3.5 Super VL model, the cost estimates from Nemotron 3 Super pricing reported by OpenRouter was used as a proxy; they do not represent measured Nemotron 3.5 Super VL serving costs. Claude results were collected separately by Aible using OpenRouter and exclude prompt-caching benefits. These differences should be considered when interpreting the comparisons.

But we wanted to ask a different question - how can we objectively evaluate the value of the output of a DGX Station deskside supercomputer running Nemotron 3.5 Super VL? 

To achieve this, we drew inspiration from the Big Mac Index - the lighthearted economic tool created by The Economist in September 1986 to measure the purchasing power parity (PPP) between nations. Instead of using a Big Mac as the scale, we used the value of a MVA analytics agent's output as the scale. We valued each analytics task at $2.324 because that was the cost of the task with Claude Code / Opus 5.5.

If we used this task and the corresponding Claude/Opus cost as a benchmark, how much work could a DGX Station produce? To test this we benchmarked the DGX Station with a single GB300 running NVIDIA Nemotron 3.5 Super VL and tried to produce as many analytics tasks as possible in a 5 minute period.




Thus, a single DGX Station if serving analytics tasks for 96 users in parallel continuously for a year would generate 18 Million analytics tasks with a value of $42 Million (valuing each task at $2.324 as per the benchmark above).

Note that the value of the task here would vary by the nature of the task and the comparison frontier model and harness. If you want to see what the cost would be for your favorite harness and model, go to the Minimum Viable Agency Benchmark Challenge page, access the dataset and methodology for the analytics use case, and see what is the cost for the analytics task (our “Big Mac”) for your favorite harness/model. Then multiply18 Million by the cost of the task for your favorite harness/model. That would be your value of the DGX Station running continuously for an year performing such tasks.

Alternatively, please contact us here and we can walk you through how to conduct a similar benchmark and see what would be the value of a DGX Station for your unique mix of use cases.