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Nvidia's Project Digits is a 'personal AI supercomputer'

(techcrunch.com)
623 points magicalhippo | 5 comments | 07 Jan 25 04:14 UTC | HN request time: 0.438s | source
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Karupan ◴[07 Jan 25 04:41 UTC] No.42619320[source]▶
I feel this is bigger than the 5x series GPUs. Given the craze around AI/LLMs, this can also potentially eat into Apple’s slice of the enthusiast AI dev segment once the M4 Max/Ultra Mac minis are released. I sure wished I held some Nvidia stocks, they seem to be doing everything right in the last few years!
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dagmx ◴[07 Jan 25 04:45 UTC] No.42619339[source]▶
I think the enthusiast side of things is a negligible part of the market.

That said, enthusiasts do help drive a lot of the improvements to the tech stack so if they start using this, it’ll entrench NVIDIA even more.

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Karupan ◴[07 Jan 25 05:20 UTC] No.42619510[source]▶
I’m not so sure it’s negligible. My anecdotal experience is that since Apple Silicon chips were found to be “ok” enough to run inference with MLX, more non-technical people in my circle have asked me how they can run LLMs on their macs.

Surely a smaller market than gamers or datacenters for sure.

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1. dagmx ◴[07 Jan 25 05:43 UTC] No.42619637[source]▶
I mean negligible to their bottom line. There may be tons of units bought or not, but the margin on a single datacenter system would buy tens of these.

It’s purely an ecosystem play imho. It benefits the kind of people who will go on to make potentially cool things and will stay loyal.

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2. htrp ◴[07 Jan 25 06:23 UTC] No.42619863[source]▶
>It’s purely an ecosystem play imho. It benefits the kind of people who will go on to make potentially cool things and will stay loyal.

100%

The people who prototype on a 3k workstation will also be the people who decide how to architect for a 3k GPU buildout for model training.

3. mrlongroots ◴[07 Jan 25 11:41 UTC] No.42621450[source]▶
> It’s purely an ecosystem play imho. It benefits the kind of people who will go on to make potentially cool things and will stay loyal.

It will be massive for research labs. Most academics have to jump through a lot of hoops to get to play with not just CUDA, but also GPUDirect/RDMA/Infiniband etc. If you get older/donated hardware, you may have a large cluster but not newer features.

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4. ckemere ◴[07 Jan 25 14:17 UTC] No.42622526[source]▶
Academic minimal-bureaucracy purchasing card limit is about $4k, so pricing is convenient*2.
5. bwfan123 ◴[07 Jan 25 15:46 UTC] No.42623624[source]▶
Devalapers developers developers - balmer monkey dance - the key to be entrenched is the platform ecosystem.

Also why aws is giving trainium credits for free