They tried and failed.
They tried and failed. xAi made a mistake building Colossus 1 and ended up with heterogenous cluster of H100/H200/GB200 GPUs. This is a nightmare to train huge models on because each card has different specs, features, and hardware requirements. During gradient synchronization, a heterogeneous cluster would bottleneck on the slowest GPU (H100) so the faster GPUs would end up idling. They also probably ran into unexpected compatibility issues, which are difficult to resolve. It makes more sense to use this cluster for inference, since they can segment the cluster by GPU type and avoid GPU mixing. xAI doesn't have enough inference customers so it makes sense to monetize this to companies that need inference compute such as Anthropic or Cursor. Apparently xAI will try building SOTA models on Colossus 2, which will be built on Blackwell GPUs only.
Posted by tristanj on May 21, 2026, 9:50 AM
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Raise real-world problems, find people facing the same issue, and discuss solutions.2,000 issues0 comments
ux (Other)
To be fair, Inform7 can't succeed, generally, because there is no market for the kind of games that you create with Inform7.
New> The notion that somehow LR reliability is misunderstood by Consumer Reports, JD Power, Car & Driver, Automobile etc.
Custom trigger IDs are not displayed in the new 2026.10 trigger selector
I hate that too, but I think it’s more the ux than the concept of a default.
> The biggest problem with UX developers is they know better than you AND force you into their paradigm.
"Focus follows mouse" is what I use to keep the file manager from popping up when click-dragging files.
The biggest problem with this is if the target URL doesn't load but also doesn't quickly error out, like e.g.
Google wants to build up a private web here.
They tried and failed.
They tried and failed. xAi made a mistake building Colossus 1 and ended up with heterogenous cluster of H100/H200/GB200 GPUs. This is a nightmare to train huge models on because each card has different specs, features, and hardware requirements. During gradient synchronization, a heterogeneous cluster would bottleneck on the slowest GPU (H100) so the faster GPUs would end up idling. They also probably ran into unexpected compatibility issues, which are difficult to resolve. It makes more sense to use this cluster for inference, since they can segment the cluster by GPU type and avoid GPU mixing. xAI doesn't have enough inference customers so it makes sense to monetize this to companies that need inference compute such as Anthropic or Cursor. Apparently xAI will try building SOTA models on Colossus 2, which will be built on Blackwell GPUs only.
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