Open Source AI Strikes Back — Inside Ai2’s OLMo 3 ‘Thinking"
episodePreviously titled “Can America Win the Open Source AI Race? — Olmo 3 with Ai2’s Nathan Lambert & Luca Soldaini” — renamed by the publisher on Aug 2, 2026
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What is the OLMo 3 model family and why is it significant?
There was a big change in leadership at Meta and Llama's future is unknown. So there's this big vacuum of influence which has been absorbed by the likes of Quen, Deep Sea, Kimmy Moonshot in terms of like who's trying to build things with open models. And that's a big shift.
We're launching OMO three family today. And just like every single models that we released before, we're not just releasing the final models. We're putting out all the details.
It's like the first fully open reasoning model where we show doing RL on base models and distilling from bigger thinking models. And there's a lot of discussion within the US that there's like good reason that we should own the whole technological stack and that includes open models. There are people that are really starting to wake
up. To this. Hi, I'm Matt Turk. Welcome to the Matt Podcast. Today we have a special episode with Nathan Lambert and Lucas Aldeny from the Allen Institute for AI for the release of the Almo 3 model family. At a time when most open source releases are just open weights, AI2 is going all in on real openness, models, data, recipes, and intermediate checkpoints. In this conversation, we break down Almost 3's architecture, the rise of thinking models, and The increasingly high-stakes race between US open source efforts and fast advancing Chinese powerhouses like Quen, Dipsic, and Kimi. This is a rare, fully transparent look at how modern AI models actually work. Please enjoy this great episode with Nathan and Luca.
Guys, welcome to the pod. Uh big announcement today and a big day for open source AI. Walk us through what it is that you're releasing today.
Thanks for having us. Yeah, we're launching OMO three family today. Um so this is our latest uh family of open source models. We have a 7B model, a 32B model, we have models that can think, models that can follow instruction and use tools. And just like every single models that we released before, we're not just releasing the final models. We're releasing, you know, the entire recipe we follow to get this model. So the data, the intermediate states, The evaluation frameworks, all the details, all the bits that uh people need to know to make uh models like OLMO.
Specifically there's uh Ohmo three bays, uh seven B and thirty two B. So what what are those? Probably we have say five
You know, flagship uh checkpoints that we're putting out. Two of them are base models. That means these are models before they get trained to respond to user instruction. Um, so these are really good for folks who want to take a sort of bulk of our compute that we spend in pre-training these models and then they want to customize them for their use cases. So these are a two-base model, there are smaller ones, there's more efficient, uh, that takes about one GP. to fine-tune for use case. And then there's a larger 32B that takes about one box of A GPUs to fine-tune. And then on top of that we have our fine tune our uh post-trained models for various use cases. So there's models, there are a couple of models that are thinking models.
Um so there's almost 7B think and almost 32b think. Um these are models that um you know just like a lot of the reasoner or like pro models out there, um they can spend um compute power uh inference time um to sort of think through a problem and solve it and then give you an answer at the end. Um and also we are releasing a seven B instruct model. This is a more like immediate model that gives you faster um response. So it's really good for like bulk data processing or or use cases where like you want to have like low latency in your responses.
I want to add more color to these things. I think Luca's underselling their base model. Um, we're gonna talk more about this, but over this year, a lot more people have been releasing open models and especially large open models. But um, some people are starting to like not release base models. We have a bunch of like deep seek size giant MOE base models and a bunch of small base models. But for example, Quent 3, which everyone accepts as like a research standard and an industry standard.
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Chapters
6 chapters
1
What is the OLMo 3 model family and why is it significant?
0:00–15:18
2
How does AI2 define “true open source” and why is it rare?
15:18–31:11
3
What role do intermediate checkpoints and transparency play in OLMo 3?
31:11–49:59
4
Why are Chinese labs like Qwen, DeepSeek, and Kimi leading the open‑source AI race?
49:59–1:06:01
5
What are “thinking models” and how does inference‑time scaling improve reasoning?
1:06:01–1:24:00
6
How does the six‑stage training pipeline (pre‑training, mid‑training, long‑context, SFT, DPO, RLVR) work?
1:24:00–1:28:10
Speakers
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