Interesting that in the article, they do not say what the chip actually does. Not even once.

the real news is: "and teases an Nvidia-friendly roadmap"

The sole reason amazon is throwing any money at this is because they think they can do to AI what they did with logistics and shipping in an effort to slash costs leading into a recession (we cant fire anyone else.) The hubris is magnanimous to say the least.

but the total confidence is very low...so "Nvidia friendly" is face saving to ensure no bridges they currently cross for AWS profit get burned.

I've had to repeatedly tell our AWS account reps that we're not even a little interested in the Trainium or Inferentia instances unless they have a provably reliable track record of working with the standard libraries we have to use like Transformers and PyTorch.

I know they claim they work, but that's only on their happy path with their very specific AMI's and the nightmare that is the neuron SDK. You try to do any real work with them and use your own dependencies and things tend to fall apart immediately.

It was just in the past couple years that it really became worthwhile to use TPU's if you're on GCP and that's only with the huge investment on Google's part into software support. I'm not going to sink hours and hours into beta testing AWS's software just to use their chips.

Amazon post: https://www.aboutamazon.com/news/aws/trainium-3-ultraserver-...

AWS keeps making grand statements about Trainium but not a single customer comes on stage to say how amazing it is. Everyone I talked to that tries it says there were too many headaches and they moved on. AWS pushes it hard but “more price performant” isn’t a benefit if it’s a major PITA to deploy and run relative to other options. Chips without a quality developer experience isn’t gonna work.

Seems AWS is using this heavily internally, which makes sense, but not observing it getting traction outside that. Glad to see Amazon investing there though.