The New Stack for General Purpose Robotics: Tearing Down the AI Complexity Wall
The biggest challenge in building general-purpose robots isn’t just the model architecture. A big part of the challenge is the brittle, fragmented infrastructure behind them. For too long, robotics researchers have been forced to double as DevOps engineers, stitching together “Frankenstein stacks” just to keep experiments running.
In this panel, Physical AI leaders will share how they simplified their setups and made them more reliable and easier to scale. We’ll discuss common issues, idle GPUs, crashed training runs, rising cloud costs, and what changed after rebuilding their infrastructure.
We will discuss:
- How to transition from “it works on my machine” to “it scales to 1,000 GPUs” with zero code changes
- The impact of unified tooling (across Data, Training, SimulationServing) on research-to-production timelines
- Why the next generation of Physical AI winners will be defined by their ability to scale compute without scaling their DevOps headcount
Fill out the form to register and get the recording
Try Nebius AI Cloud console today
Get immediate access to NVIDIA® GPUs, along with CPU resources, storage and additional services through our user-friendly self-service console.
