alt Hacker News> one NVIDIA GB200 NVL72
That’s around ~$4-6 million in hardware cost (one rack, 72 GPUs + accompanying hardware).
Many AI shops are funded well into tens of millions if not hundreds, for early rounds.
Also the team size is really small (< 10???). So yes, indie might be a stretch but still very impressive, relatively speaking.
Now taking the other side, they didn’t have to worry about hosting/billing/subscriptions or any of that yet. It’s a research demo at this point.
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Also a speech to speech model it can't do any tool use or you can't guide it much am I mistaken? How will it be used for agentic uses is the part I don't get. Edit: this is response from Argon if anyone was also curious: You are not mistaken about Dessa: because it was trained solely on raw audio with zero text data, it cannot read text system prompts or output code for tool calls on its own. To use it as an agent, developers must either steer it purely through spoken voice instructions or pair it as a conversational "voice front-end" alongside a separate text LLM that listens to the audio and executes the tools in the background.
> they didn’t have to worry about hosting/billing/subscriptions or any of that yet
Or marketing, which they kinda get for free. So much research goes unseen, there’s huge competition for attention, and most research is very limited in that regard
I would say that’s the biggest factor that makes the team “not indie”
At the same time, like you said, still amazing accomplishment, very cool model, and great work
oh, hacker news, where someone will act like a ten person shop with a seven million dollar computer might be indie
Perhaps boutique is a better term?
> > one NVIDIA GB200 NVL72 That’s around ~$4-6 million in hardware cost (one rack, 72 GPUs + accompanying hardware).
that’s a good clarification, skimming some of the comments here I was thinking they trained it on a single GB200 gpu. THAT would be a huge win for indie AI
Edit: Nvidia GB200 does not refer to a single gpu, Nvidia’s naming scheme is very confusing. Considering past gpus were called H100, H200 etc, I thought GB200 would just be a newer GPU, but it does actually refer to their 30+ gpu rack scale systems. The claim of “we trained this on a single rack of gpus” is definitely much less impressive than “we trained this on a single gpu”