tinygrad

“Tiny grad is, we are going to make a risk offset for all ML models. And yeah, it can run all ML models with basically 25 instead of the two 50 of XLA or PrimTorch. So about 10 X less complex.”

“We're below Onyx runtime, but we're beyond CoreML. So, like, that's, like, where we are in Onyx support now, but we will pass Onyx runtime soon, because it becomes very easy to add ops, because of how, like, you don't need to do anything at the lower levels.”

“The correctness for both forwards and backwards passes is there, but on Nvidia, it's about five X slower than PyTorch right now.”

“TinyGrad is used to run the model in OpenPilot. Like right now, it's been live in production now for six months. And TinyGrad is about two X faster on the GPU than Qualcomm's library.”

“Replicating the PyTorch API. Is something I can do with a couple, you know, like an engineer month or two.”

“There's no, like, I don't think, like, unless we have, like, great breakthroughs, like, George's vision is achievable, like, or, like, he should be thinking about a narrower problem, such as, I'm only gonna make this for, like, work for self-driving car con ne…”