Live data from Hacker News

Nvidia DGX Spark: great hardware, early days for the ecosystem

simonwillison.net

111–116 of 116 posts

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#111
post #106

Is there like an affiliate link or something where I can just buy one? Nvidia’s site says sold out, PNY invites you to find a retailer, the other links from nvidia didn’t seem to go anywhere. Can one just click to buy it somewhere?

My local reseller has them in stock in the EU with a markup... Directly from Nvidia probably not for quite sometime I have some friends who put in preorders and they didn't get any from the first charge.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#112
Added this to my benchmark site as seems that we might see a lot of purpose build desktop systems going forward.

You CAN build - but for people wanting to get started this could be a real viable option.

Perhaps less so though with Apple's M5? Let's see...

https://flopper.io/gpu/nvidia-dgx-spark

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#113
post #83
post #59

Earlier quoted context omitted.

As someone who hot on early on the Ryzen AI 395+, are there any added value for the DGX Spark beside having cuda (compared to ROCm/vulkan)? I feel Nvidia fumbled the marketing, either making it sound like an inference miracle, or a dev toolkit (then again not enough to differentiate it from the superior AGX Thor). I am curious about where you find its main value, and how would it fit within your tooling, and use case…

M3 Ultra has slow GPU and no HW FP4 support so its initial token decoding is going to be slow, practically unusable for 100k+ context sizes. For token generation that is memory bound M3 Ultra would be much faster, but who wants to wait 15 minutes to read the context? Spark will be much faster for initial token processing, giving you a much better time to first token, but then 3x slower (273 vs 800GB/s) in token gener…

This is 100% the truth, and I am really puzzled to see people push Strix Halo so much for local inference. For about $1200 more you can just build a DDR5 + 5090 machine that will crush a Strix Halo with just about every MoE model (equal decode and 10-20x faster prefill for large, and huge gaps for any MoE that fits in 32GB VRAM). I'd have a lot more confidence in reselling a 5090 in the future than a Strix Halo machine, too.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#114
post #106

Is there like an affiliate link or something where I can just buy one? Nvidia’s site says sold out, PNY invites you to find a retailer, the other links from nvidia didn’t seem to go anywhere. Can one just click to buy it somewhere?

It still isn't at distributors yet. My distributor has it listed for Oct 27, with units shipping the day after from the warehouse to resellers/etc.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#115
post #3

As is usual for NVidia: great hardware, an effing nightmare figuring out how to setup the pile of crap they call software.

If you think their software is bad try using any other vendor , makes nvidia looks amazing. Apple is only one close

Pretty much this. Nvidia isn't big because of their hardware, they're not ahead on that front. It's their software support that makes it worthwhile.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#116
post #78

Earlier quoted context omitted.

What do you mean by "kneecapped for training"? Isn't it 128GB of VRAM enougth for small model training, that a current GC can't do? Obviously, even with connectx, it's only 240Gi of VRAM, so no big models can be trained.

Spend some time looking at the real benchmarks before writing nonsense

You are quite rude here. I was asking questions. The benchmarks are very new and don't explains why it can used for training.

But if FP4 means 4bit floating point, and that the hardware capability of the DGX Spark is effectively only in FP4, then yes. That was nonsense to wish it could have been used for training. But it wasn't obvious from the advertising of nvidia.

Post reply on HN