Nvidia’s GPUs Have a Weakness: Video Generation

There’s no question that Nvidia has benefitted the most from the AI wave so far. So much so, in fact, that the U.S. Department of Justice is investigating complaints that the chipmaker is engaging in anticompetitive behavior, Anissa, Amir and I reported last week.

But luckily for the host of rivals hoping to take a slice of Nvidia’s lucrative pie, the company’s popular chips do apparently have a weakness when it comes to a growing field of AI research: video generation.

The main issue is that Nvidia’s graphics processing units, even its popular H100s, don’t have enough memory to handle the immense amount of video data needed to train these models, founders of AI video startups tell me.

“Training data [for video generation models] can scale into petabytes pretty easily, as opposed to a few hundred terabytes for language-only models,” said Amit Jain, a cofounder at video generation startup Luma AI. To give you a sense of the magnitude of that—a petabyte is equivalent to a whopping 1,000 terabytes, or around 500,000 hours of video.