xAI Is Reportedly Using Just 11% of Its 550k Nvidia GPUs

alexdumny

This is the exect information I am looking for

aggakake

Aren't Xai's datacenters powered by [currently very expensive] diesel?

dlcarrier

That's a problem that any general purpose design has. It's something Dojo would have fixed, but it went too far in the other direction and only supported training. Rumor has it the new version will support inference too.

londons_explore

Part of this is a human problem. The company wants better utilisation, so hires resourcing experts tasked to allocate resources between projects and teams. These experts set up quota systems, priority allocation, month-ahead plans, burst and idle quotas, etc, all with a goal to get the resource better used. However it ends up having the reverse effect - teams now waste the resource deliberately to make it appear they have better utilisation, and run pointless jobs because "use it or lose it" quota systems discourage being thrifty. These problems are compounded by there being hundreds of resource types - "I've got plenty of CPU and GPU TFlops for my project, but I've run out of disk spindle hours so can't run the training job". End result is that the company as a whole doesn't even know real utilisation, and makes exceptionally poor use of resources.

Frannky

Grok is pretty bad. No wonder usage is low. I think they messed up when they removed the human annotation team and went in the direction of automation. The bet can eventually pay off when they figure out how to train without human help and also generate useful models. Imagine is terrible too. More competition is great for us users. I hope they recover. In the meantime why not hosting oss models like google does?