Originally published at: Optimizing Data Center Performance with AI Agents and the OODA Loop Strategy | NVIDIA Technical Blog
For any data center, operating large, complex GPU clusters is not for the faint of heart! There is a tremendous amount of complexity. Cooling, power, networking, and even such benign things like fan replacement cycles all must be managed effectively and governed well in accelerated computing data centers. Managing all of this requires an accelerated…
Hope anyone here finds this useful. Happy to answer any questions about our approach, the models involved, and the challenges we’ve had along the way to making this possible.
Hi Aaron,
Great to see Nvidia embracing Agentic OODA! Back in the '80s, OODA was developed for AI robotics, but compute was not ready.
We have a new worldwide OODA Research circle centered on UTexas Humanoid Robotics (AE, CS, ME, EE) and IEEE-RAS. Several of us are in Nvidia’s Developer Community. How can we collaborate?
TIA.
Dave
Texas
Would be happy to collaborate on this, very fascinating what you are working on.
Great project with great applicability across many domains and industry verticals. I lead Edge AI architecture team at Verizon and we are exploring, together with partners, building blueprint GenAI architectures for different use cases that benefit from using 5G networks to ingest the swath of sensor data required to enable such use cases. Industrial systems/workflows observability is a common and low hanging fruit use case I see across our customer/prospect base. This particular POC could easily be adapted to address such use case - the architectural patten is very applicable. would be glad to connect offline and explore possible avenue for collaborations.
Thanks Aaron,
The group is excited over your astute identification of Agentic OODA now that compute is scaling up to the potential. We will dive deep into your suggestions and stay in touch.