[mentalically]

The value proposition of Cerebras is that they can compile existing graphs to their hardware and allow inference at lower costs and higher efficiencies. The title does not say anything about creating or optimizing new architectures from scratch.

[germanjoey]

the title says "Cerebras Trains Llama Models"...

[mentalically]

That's correct and if you read the whole thing you will realize that it is followed by "... to leap over GPUs" which indicates that they're not literally referring to optimizing the weights of the graph on a new architecture or freshly initialized variables on existing ones.

[pama]

This is as clickbaity as it gets.

Trains has no other sensible interpretation in the context of LLM models. My impression was that they trained the models to be better than the models trained by GPUs, presumably because they trained faster and managed to train for longer than Meta, but this interpretation was far from the content.

Also interesting to see the ommission of deepinfra from the price table, presumably because it would be cheaper than Cerebras, though I didnt even bother to check at that point because I hate these cheap clickbaity pieces that attempt to enrich some player at the cost of everyone’s time or money.

Good luck with their IPO. We need competition but we dont need confusion.

[mentalically]

What are you confused about? Their value proposition is very simple and obvious, custom hardware with a compiler that transforms existing graphs into a format that can run at lower cost and higher efficiency because it utilizes a special instruction set only available on Cerebras silicon.

[fancyfredbot]

The title is clickbait but that's how marketing works whether we like it or not. The achievement is real - Cerberas improved their software and the inference is much faster as a result. I find it easy to forgive annoying marketing tactics when they're being used to promote something cool.

[pama]

It is textbook bait and switch. If the achievemt is important, use the correct title. An advance in actual training performance or a better model is very important and interests a different set of people with deeper pockets than those who care about inference.