It looks like people in this thread are confusing fleet utilization and MFU. If they're doing a lot of RL, it's really not surprising to see such low numbers.

Why is everyone stating conjecture based answers like my 10 year old kid with absolutely no evidence to back it?

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

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.

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?

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.

“Elon Musk's xAI, the software firm behind Gorq” - this is not an autocorrect error.

This is the exect information I am looking for