Zyphra

“Well, kernel is a last resort. So first I go, oh, another kernel. And I try and find some way to go around it.”

“It would be great, but it's not a silver bullet because kernels are also hard to validate. It's hard to have like an eval in kernels. Every time that someone releases like, oh, we created a new eval that measures kernels and we trained a model that generates k…”

“Getting Below Triton or level or anything else down to like CUDA or below there's orders of magnitude less public good kernels at that level. And I think that really shows and models capabilities. So when I try and get a model to do something in CUDA or PTX or…”

“We recently moved all of our training cluster over to AMD. So we're really going all in on AMD ecosystem.”

“We released a Zamba two, which was a hybrid between transformers and a Mamba two blocks. And we were able to be like a Lama three eight B for example, with a seven B model.”

“We found that it's great for flash attention to specifically, we were able to be H-one hundred. We also found that like the less time you spend in like dense compute, like the less time you spend in tensor cores specifically, or less time you spend in lower bi…”

“I don't really care if someone knows CUDA kernel writing. If someone does string theory and is really good at understanding complex problems, they will be productive faster than someone who knows CUDA and doesn't really care about trying to get better at it.”

“No AI in the interview, first off. And you gotta watch people's eyes now on what monitors they're looking at behind the screen, which is, that's changed in the last two or three years, unfortunately. No AI allowed.”