00:13:32 · Rebecca Balan, Irene Solaiman
[00:13:32] S1: Yeah. Yeah. So how can a, like, a startup manage this? Right? How can they know whether a cheaper model and its surrounding tools are are good enough for its product cheap enough for its product if there's no reliable way to measure, you know, the spend and ROI and just like what you're getting? [00:13:49] S2: It's gonna be a trade off for the near term and long term. In the near term, it might be easier just to run inference, to call a friend to your lab. If you have, like, high intensity tasks, if you don't have a lot of AI experts on hand, totally understandable to use what's easy, and a lot of the time, that's gonna be closed. If you want something that you wanna scale up a bit, you know you'll use it longer term, and you can... This is why a lot of folks are buying GPUs and not renting. You can set it up on prem. You can optimize that model over time without renting out the cost of compute going up. Your long term costs will likely plateau or decrease, desirable depending on the business that you're running. And for the choices that you're making for high intensity coding tasks, the latest open weight models, Kimi K3 DeepSeek v4.1 Flash and the Qwen 3.8 Max are optimized for coding tasks. So you can look at these types of benchmarks. You can look at the types of coding tasks you're interested in. Or if, again, you have these skills, you can fine tune that model. There are a lot on Hugging Face, a lot of data sets for fine tuning to optimize for certain tasks.
00:15:04 · Rebecca Balan
[00:15:04] S1: You mentioned... Just wanna quickly dig into this. You mentioned that, you know, a lot of people are buying their own GPUs these days. Right? Yeah. I'm kinda curious about that. Like, you have any tips for... Because, you know, where all we hear about is GPU scarcity and it's, you know, let's say I'm a startup founder. I need to mess around. I need to do a lot of experimentation. I'm using open models. I wanna have my own, you know, GPU situation going on. Am I buying, like, how many GPUs would I need to buy? And is it okay to, you know, not get the latest greatest NVIDIA chip because you probably can't anyway?
00:15:36 · Irene Solaiman, Rebecca Balan
[00:15:36] S2: I mean, yeah, the scarcity is not just to your point, the the availability, but the availability of what. And a lot of... There is a hunger for the latest NVIDIA chip because a lot of the newest models run easiest on them. In terms of what you can get your hands on, there are just a lot more libraries and resources, including on Hugging Face, and generally, this is the beauty of the open source ecosystem that will help you adapt your model to that chip. So we see these previous iterations of GPUs, so like the NVIDIA Blackwell, a lot of folks use for frontier models, and this is... We're gonna get a little bit technical here. It's also a question of the latency for what you're deploying, and this is the level of speed of response. For our incident over the summer, we deployed a NVIDIA quantized, so this is NVFP4, it's a four bit quantized model. It's not going to have that same level of sort of horsepower that a fully high speed kind of, inference will give you, but it'll get the job done, and it'll cost significantly less than what we estimate a... What we estimate the agents' warm cost. [00:16:50] S0: This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed Sponsored Jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed Sponsored Jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.
00:17:20 · Rebecca Balan
[00:17:20] S1: And and for the listener, if you don't know what Irene is referring to with the incident, the incident is obviously when OpenAI's, agent swarm, accidentally kind of hacked into Hugging Face. And Hugging Face was able to defend itself using some some open models rather than the closed models, which I think threw up some guardrails, which is something that we're... We'll we'll actually talk about that again because I wanna talk to you about the open code attack. But... And then another disclaimer, NVIDIA has acquired or is in the process of acquiring Hugging Face as well. So just, you know, note that. Another thing about NVIDIA, and I'm gonna come to it in a roundabout way, I wanna talk a little bit about China and competition for open model dominance. You know, the best open models, you've listed several of them. They're all Chinese at this point. What happens if Chinese models become the default for AI startups around the world? And actually, not just AI startups, like sovereign AI for governments. And, you know, we're seeing Rio De Janeiro in Japan's sovereign models are built on Qwen. So what is that... What happens? Is that a problem?
00:18:24 · Irene Solaiman
[00:18:24] S2: It's a... It's always a dance when we're talking about open models on... With the geopolitics. The geopolitics have become much more central even the last few months. It's a question of problem for whom. So people love open models no matter where they come from. I've had to scale up very quickly with our head of APAC. It's really just me and Adina working with Open Frontier Labs, these Chinese labs who are so talented, who work really hard, and, it's incredible to feel the ethos of belief in open weights. It would be even more exceptional to have a thriving global ecosystem and to have Open Frontier US models. We are working closely with with the Muse team, with Reflection, with RC. It's really hard to train an open frontier model, but there's a reason that governments are preferring models developed in The US, for example. There's a reason that GPTOSS still has a large amount of downloads on Hugging Face. There is a hunger for US models. How do we stoke that, I think, is a big a big question around the narrative. There's been a demonization of open source of them being... These models being more dangerous, which if you look at... Thanks for the the explanations and disclaimers, Rebecca, the last few months of cyber attacks, the most prominent ones have been frontier models, have been close weight models. And we're just balancing with what do we need to make sure that folks get access and have the tools in hand. And a lot of that... A lot of the time, it's open weight.
00:19:58 · Rebecca Balan, Irene Solaiman
[00:19:58] S1: You said that there's, like, know, there's a hunger for GPTOSS. There's a hunger for American open models. What... So... And a lot of people, a lot of companies, governments want to build on American models rather than Chinese. What what is the main reason? Is it just, like, the geopolitics of it all? [00:20:13] S2: So for... Also for context, g two s s was released by OpenAI in around Yes. August 2025. That's that's my bad for not to wait. [00:20:22] S1: Sometimes I'm like, we're all nerds here. Right? We all we all know what we're talking about. Right? [00:20:27] S2: But there have just been so many that I'd measure time and latest frontier generation. [00:20:31] S1: Believe that was 2025. No. [00:20:34] S2: August 2025 was due [00:20:36] S1: to open AI. When's the next open model? Come [00:20:39] S2: on now. We're also, you know, itching over here. [00:20:42] S1: Yeah.