Comment by toddmorey

> > 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”

> 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

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.

Wait, seriously, a rack of 72 GPUs costs $4 million?

Perhaps boutique is a better term?