Alexis Crowell

"if you look at the life cycle of that model, it's 31x on the inference side than it is on the training side, because you constantly run it."

"we think inference is going to be the future of what gets run the most. So that's what we do. We do inference specifically, started at edge, but now we're actually scaling into the data centers."

"Well, and maybe within that, one piece that we haven't touched on is where is the regulation on some of the more regulated industries, right? Medical, you kind of hit on, is there a verticalized solution."

"I think medical is a great scenario for this because there's a scarcity of clinicians. According to the World Health Organization, there's 10 million fewer clinicians than what we need worldwide. If you bring AI into that, now not only can you read radiology scans faster, you can point doctors towards, hey, maybe you haven't seen this type of disease."

"we're putting 630 tops in a 35 watt chip. So are we solving GPT- 4? No, we're not. But if you've got a 7 to a 70 billion parameter model, you can have that running right next to you."

"There's not really been a good success story of someone starting in a data center and being able to scale that infrastructure into an edge environment. So we think we've got the starting point right because now we have performance efficiency and power efficiency at high performance that can now scale into data center solutions."