Nvidia open-sources Flora to make robot video labeling 125x cheaper than GPT-6 Astra

What happened

And third, judgments on whether a task was completed successfully, used to monitor robots during deployment. Nvidia's new data engine Flora labels one hour of robot video for 72 cents instead of $88 with GPT-6 (large language model by OpenAI) Astra, while scoring slightly higher on quality. NVIDIA is a semiconductor company based in Santa Clara, and its products and services include graphics processing units.

The open system, built on three fine-tuned (further training of an existing model for a narrower job) Qwen3.5 models, catches errors in robot actions far more reliably than any competitor but sometimes falls short on descriptive accuracy. Nvidia is positioning Flora as a key piece of its strategy to control the entire robotics value chain.

But the comparison with GPT-6 Astra isn't quite as lopsided as the price tag suggests. Large AI models for robots learn from videos that need text annotations, and according to Nvidia's GEAR research lab, this requires three types of labels.

First, descriptions of what action happens when in the video, used to select training data (the material a model is built from). Second, ratings of how well a robot progresses step by step, so it can learn through trial and error.

Most existing tools only provide one of these types. Large language models like GPT-6 Astra can generate several, but they're too expensive for the massive volumes of video involved.

According to the paper , Flora covers all three. Nvidia says the system is already running during training of its GR00T models, annotating up to 200,000 hours of video per day across 512 GB300 GPUs (a chip built for many calculations at once, used for graphics and AI).