Jeff Schmidt
Co-founder and CTO, Nous Research. On 1 show, 1 appearance. The Shows tab opens the full record on each.
founderexecutivescientistengineer@theemozilla ↗jeffq.com ↗
He co-founded Nous Research, an open-source AI research collective and lab known for open-weight models including the Hermes model family, YaRN, and DisTrO. He focuses on building decentralized, user-steerable, and community-trained AI systems designed to operate without centralized corporate guardrails.
1shows
1appearances
23statements
5resolved
3supported
1contradicted
60%fully supported
Everything Jeff Schmidt said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Schmidt: AI industry relies on outdated 1990s architecture assumptions ripe for disruption
“It turns out that most assumptions in the AI space right now are a product of that's just how things had been done when there was not nearly as much energy and attention to it. So someone made an assumption maybe in the early nineties that everyone just kind o…”
Schmidt: Relying solely on fine-tuning is an existential threat to open-source AI
“Because that for us, if we're just fine tuning models. Right. That's like an existential threat, right? That's like an actual existential threat because the closed providers will continue to get better and we would be like dead in the water in a lot of sense.”
Schmidt: Sharing only key signals in distributed training yields equivalent model learning
“We know that like what we, what needs to be communicated between these things, the two, the different nodes are just these few key pieces of information. And that is necessary. That is a necessary condition or rather a sufficient condition To get the equivalen…”
Schmidt: Decentralized training will force NVIDIA to redesign chips around VRAM ratios
“What might happen sooner would be a redesign of the types of chips that NVIDIA or someone would make. Okay, under this model, we can dedicate more VRAM versus, there's like this question of how much VRAM versus how much processing power is on a die, and that, …”
Schmidt: Compute chips in NVIDIA's RTX 4090 and H100 are almost identical
“I think people don't actually realize that like a forty-ninety and like an H 100 are in a lot of ways the same card. For the non-gamers in the room, explain the forty-ninety. The chip that's inside of them is almost identical. The chip, the actual compute chip…”
Schmidt: Consumer gaming GPUs will become the sweet spot for distributed training
“Because you're able to distribute it so wide, I think the gaming GPU angle is really going to be like the sweet spot. If, as long as there's continued to be sort of like higher end gaming GPUs, and those are on comparison with the high end training GPUs, even …”
Schmidt: Decentralized training of 400B parameter AI models is solvable by 2025
“I think it still is, it would still be, you know, like a next year sort of environment thing that we would have to do. There are some scaling problems, or not scaling problems, but technical things about how you shard the model, because at that point you get t…”
DisTrO trains multiple models in a bounded search space instead of full synchronization
“So with distro, what we found is that rather than bringing everyone back home and averaging it back together, what you want to do is give each of those little nodes that are searching for the lowest point in the lost landscape, the freedom to move around. And …”
Schmidt: DisTrO will eliminate data centers' InfiniBand dependency before edge AI dominates
“I think there's, you know, immediately coming out. What you'll see is the ability for even centralized actors who might have multiple data centers to now, like just use them in a more efficient way. Like just have N equals two, you know, like anything. And eac…”
Schmidt: ChatGPT, Llama, and DeepSeek use Nous Research's YaRN context extension
“Bone here is the lead author of a method we developed called YARN, which is a context window extension method that we released and did the research on. It is now used by every, every model you use nowadays, everything, everything Chachipiti, Lama, DeepSeq, all…”
Jeff Schmidt: Hermes pioneered synthetic data training before it was standard
“So Hermes was very early to the idea that you could have synthetic data, which is that you could actually make, you could make a better model by taking an AI model, having it generate words and text, and then training a new a model on that output. This is now …”
Schmidt: Fewer than ten organizations worldwide can train Llama-scale AI models
“Yeah, I mean, I would, it would probably be in the number of ones on my hand and it probably wouldn't use all my fingers, you know. Yeah, I mean, you basically have, OpenA, Anthropic, Meta, X, Google, and then you have a few Mistral, and then Deep Seek and a c…”
DisTrO's performance advantage over AdamW widens as models scale up
“What we have seen empirically is that as we make it bigger, the differential between distro and MW actually gets wider.”
Schmidt: Nous is building fault-tolerant training code for heterogeneous devices
“We're making sure that also like the code we're writing to help to actually do this training is agnostic to the hardware and is able to communicate and operate. You can have an Apple device and an NVIDIA device training together. And this is actually the, for …”
Jeff Schmidt: Open-source AI lags closed AI providers by 1 to 1.5 years
“It seems that we're in the open source space. We're always like a year playing catch up, like a year, it's like a year and a half, a year and a half behind like the closed providers.”
Schmidt: Synchronized AI training stems from PyTorch convenience abstractions, not optimal convergence
“But the one monolithic thing was actually just like a technical bot. Like it was from the fact that like we had PyTorch and then they like, you know, or like at Karis or any of the other ones. And they're like, Well, if you want, you can train on multiple GPUs…”
Jeff Schmidt: AI Research Offers Unprecedented Green Field for Groundbreaking Discoveries
“We're lucky right now because the state of AI as a science is incredibly new, and it truly has a wide green field. And unlike lots of other areas of science where if you look at something and you think, why hasn't somebody done X, Y, and Z? Unfortunately, ofte…”
Schmidt: Current AI training requires all GPUs in the same datacenter
“When it comes to training models, the current paradigm for training models requires that all of the GPUs that train the model, these, you know, these computers that do the training, they all have to be like in the same room.”
Nous Research replicates DisTrO training results using Allen AI's OLMo framework
“And we've re-implemented now a third time in their framework, and we're able to reproduce their training run exactly, and then did it again with Distro, got the exact same results we got with Natron and stuff.”
Schmidt: Zeroth-Order Optimization Requires 1,000x More Computation Than Backpropagation
“What we discovered is that backprop is still being like, you really do still need to be doing back propagation to find the optimal point of the loss. And it's just like zeroth order is like, what, like a thousand, like it worked, but it was like, you needed li…”
Schmidt: Elon Musk's xAI has acquired 100,000 NVIDIA H100 GPUs
“I think Elon's got a hundred, a 100,000 H 100 now.”
Nous Research has trained DisTrO models up to 7 billion parameters
“We've gone up through seven B now, like seven B models.”
One line per show, most statements first. The link opens Jeff's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 a16zLEDGER | Co-founder and CTO, Nous Research | 1 | 23 | 60% 3/5 | full record on the a16z Podcast → |