Solutions
Four ways teams use the factory
The models differ, the loop does not. Each of these teams generates data, trains on it and validates the result — they just weight the three stages differently, which is exactly what capacity planning has to account for.
01 · Embodied AI & Humanoid Robotics
The demonstrations are the bottleneck. Not the GPUs.
Teams building general-purpose robot brains — end-to-end vision-language-action models that have to transfer across humanoids, arms and mobile bases.
The problem
Real demonstrations are the scarcest resource in robotics. A teleoperation session produces a few hundred episodes; a policy that generalises wants orders of magnitude more — across lighting, object poses, materials and failure cases no operator would think to stage. The largest open real-robot dataset ever assembled, pooled from 34 labs, holds roughly a million trajectories. Most teams have a fraction of a percent of that, and collecting more means a human in a room with a robot, one episode at a time.
How the loop runs here
Your demonstrations go in; domain-randomised variations come out. World foundation models expand each episode across lighting, backgrounds, object geometry and physical properties, and an evaluator scores which generated data is fit to train on.
Pre-train a VLA model from scratch, or fine-tune an open robot foundation model such as NVIDIA Isaac GR00T on your own embodiment. Reinforcement and imitation learning run across thousands of simulated environments in parallel.
Dexterous manipulation trials in simulation before a single physical trial — grasp success, contact forces, recovery from disturbance — so the robot lab tests what already works, not what might.
What we provide
- Generation capacity that does not compete with your training jobs for GPUs
- Multi-node clusters for foundation-model pre-training, reserved by contract
- Parallel simulation for reinforcement and imitation learning at scale
- Storage sized for episode libraries that grow every week
Industry context
Open X-Embodiment pooled 60 datasets from 34 robotics labs into over 1 million real robot trajectories across 22 embodiments — still small next to the text corpora behind today's language models.
Source: Open X-Embodiment (RT-X)
NVIDIA Isaac Lab sustains 4,096 parallel environments at over 135,000 frames per second on humanoid locomotion, turning policy training from days into minutes.
Source: NVIDIA Technical Blog
02 · Autonomous Driving & World Models
The scenarios that matter are the ones that never happen.
End-to-end driving stacks and world models trained on fleet video rather than hand-written rules.
The problem
A single instrumented test vehicle can produce well over ten terabytes of sensor data in a day, and a fleet turns that into petabytes a week. Almost none of it is interesting. The frames that decide whether a release ships — the occluded pedestrian, the overturned load, the flooded intersection at dusk — appear a handful of times in a year of driving, if at all. Curation is where the compute goes, and generation is how you get the cases the fleet never happened to see.
How the loop runs here
Raw fleet video is filtered, captioned and scored at GPU speed, then world models extend the interesting fragments into scenario families: same geometry, different weather, different agent behaviour, different time of day.
Large-scale video training for end-to-end policies and world models, on multi-node clusters where the interconnect keeps the GPUs fed rather than waiting on the network.
Rollouts against held-out scenarios and regression suites, so a model that improves the average case but breaks a known edge case is caught before it reaches a vehicle.
What we provide
- Curation throughput that keeps up with what the fleet uploads
- Scenario generation for the long tail you cannot collect
- Training clusters with storage next to the compute, not across a network
- Regression evaluation on every candidate build
Industry context
One instrumented autonomous test vehicle has been measured generating 11 to 152 TB of data per day; a 200-car fleet puts that in the petabytes per day range.
Source: Tuxera
NVIDIA's pipeline curated 20 million hours of video in 14 days on Blackwell hardware — the same job runs over three years on a CPU-only pipeline.
Source: NVIDIA Newsroom
03 · Robotaxi & Autonomous Mobility
Validation is the release gate.
Driverless fleets whose software is re-validated before every release — for regulators as much as for the road.
The problem
A robotaxi stack does not ship because it passed a benchmark. It ships because every candidate build has been replayed against the whole scenario library, and because someone can show a regulator what happened in each case. That library only grows: every incident, every disengagement, every near-miss becomes a permanent test. Validation stops being an experiment and becomes a recurring industrial workload with a deadline attached.
How the loop runs here
Log replay against recorded drives, sensor simulation, and closed-loop safety runs across thousands of scenarios in parallel, with pass/fail per scenario and the trace behind every failure.
Each new failure becomes a scenario family: the same situation under variations the fleet has not driven, so the fix is proven against the class of problem rather than the single recorded instance.
Targeted post-training on the cases that failed, without disturbing what already passes.
What we provide
- Burst capacity sized to your release cadence, reserved so the deadline holds
- Parallel scenario execution rather than an overnight queue
- Reproducible runs: the same build, the same seeds, the same result
- Artefacts you can hand to a safety case, not just a dashboard number
Industry context
NVIDIA's position at GTC 2026: "Physical AI has arrived — every industrial company will become a robotics company." The validation burden arrives with it.
Source: NVIDIA Newsroom, 16 Mar 2026
04 · Industrial, Logistics & Smart Factory
The line cannot stop for data collection.
Manufacturers, warehouses and ports automating the flexible, high-mix work that fixed automation was never able to handle.
The problem
Traditional automation pays off when the part, the pose and the path never change. The work that is left over — mixed-SKU depalletising, cable routing, screw fastening on a non-standard fixture, picking something soft or transparent — changes constantly, and every site is laid out differently. You cannot halt a line for months of data collection, and a policy trained at one facility rarely survives the move to the next.
How the loop runs here
A digital twin of the actual site — CAD of the cell, the real SKUs, the real lighting — becomes the source of training data, built without touching the production line.
Manipulation and navigation policies trained against that twin, then hardened with domain randomisation so they survive the gap between the model of the site and the site.
Full-shift simulated runs and AMR fleet behaviour before a robot is installed, including the failure modes that only appear when several robots share a floor.
What we provide
- Project-shaped capacity: a burst per site or product line, then a steady trickle
- Digital-twin generation from the CAD you already have
- Re-training runs when the line changes, without rebuilding from scratch
- Results that transfer between sites rather than starting over at each one
Industry context
The humanoid robot market is projected to reach US$38 billion by 2035 — more than a sixfold increase over previous forecasts — and the industrial floor is where most of those units are aimed.
Source: NVIDIA Blog
Not on the list?
If your models touch the physical world, the loop is the same
Agriculture, mining, construction, inspection, surgical robotics, defence logistics — the workloads look different on the surface and identical underneath. Tell us what you are training and we will size it.