The Ledger, every show
Every statement that passed quotation and attribution checks, across all 44 shows. Pick shows below, then mix any filter with any other.
shows 
every show 44 of 44
Tesla Uses LiDAR During Training for Its Vision-Only Autonomous Stack
“Tesla actually does use a lot of expensive sensors. They just do it at training time. So there are a bunch of cars that drive around with LIDARs. They do a bunch of stuff that like doesn't scale and they have extra sensors, et cetera, and they do mapping and a…”
Mac Minis are uniquely suited for batch-one local LLM inference
“Mac minis, for example, are a very good fit for some of the LLMs, because it's all, if you're doing batch one inference, this is all super memory bound, so this actually works.”
Karpathy: llm.c trains GPT-2 on one H100 node in 24 hours for $600
“You can train it on a single node of H-one-hundreds in about 24 hours, and that costs roughly 600 dollars.”
Karpathy: llm.c was 20% faster and used 30% less memory than PyTorch
“At the time of that post, we were using, in LL and that's in 30% less memory, and we were 20% faster in training, just the truth.”
Tesla Autopilot neural networks systematically replaced explicit C++ code
“As we made the autopilot better, basically the neural network grew in capability and size, and in addition to that, all the C++ code was being deleted and kind of like was And a lot of the kind of capabilities and functionality that was originally written in o…”
Karpathy: Early Tesla Optimus ran car neural networks and vehicle computers
“In terms of the transfer from cars to humanoids, it was not, not that much work at all. And in fact, like the early versions of Optimus the robot it thought it was a car, like, because it had the exact same computer. It had the exact same cameras. It was reall…”
Karpathy: llm.c avoids runtime crashes by pre-allocating memory statically without dependencies
“It's a single file C. There's no dependencies whatsoever. It compiles instantly. It runs instantly. All the memory is just allocated in a single blob. So if you start stepping, there's no way you're gonna boom later. It's all preplanned. It's fully determinist…”
Karpathy: llm.c achieves nearly 50% MFU on single-node GPT-2 training
“We have almost a 50% NFU here on one node, which is quite good.”