It's just vaporware until then.
My guess is that they will use the RTX 5070 Ti laptop version (992 TFLOPS, slightly higher clocked to reach 1000 TFLOPS/ 1 PFLOP).
Their big GB200 chips have 546 GB/s to their LPDDR memory, they could use the same memory controler on the GB10. They don't need to design a new one. It would still be slower than what they are currently using on the RTX 5070 Ti laptop GPU, but any slower than that, and there is no chance that they could argue that it would hit anywhere near 1 PFLOP of FP4. It would only be possible in extreme edge case scenarios when all data will fit in it's 40MB L2 cache.
It’s also a more general comment around „AI desktop appliance“ vs homebuilts. I’d rather give NVIDIA/AMD $3k for a well adjusted local box than tinkering too much or feeding the next tech moloch, and have a hunch I’m not the only one feeling that way. Once it’s possible of course.
Just because Jensen calls it a super computer and gives it a DGX-1 design, doesn't make it one.
In the Cleo Abram interview [1], Jensen said that DIGITS is 6 times more powerful than the first DGX-1.
According to this PDF [2], DGX-1 had 170 TFLOPS of FP16 (half precision). 170x6=1020 TFLOP (~1 PFLOP). Yes DIGITS is suppose to have 1 PFLOP, but according to the presentation, it should be in FP4...
He also said that it will draw 10k times less power. But DGX-1 had a TDP of 3.5kW [3] and I highly doubt DIGITS will draw 3500/10000=0.35W... the GPU alone will have a peak TDP that is more like 200 times higher than that.
I mean, we all know that NVIDIA does fudge the numbers in charts. Like comparing FP8 from last generation, to FP4 on this. But this is extreme.
Having said that. Do I believe that they can deliver a laptop (in another form factor) and it will perform 1 PFLOP of FP4. Of course! Like I said, it is nothing special. Both Apple and AMD have unified memory in relatively cheap systems.
1. https://youtu.be/7ARBJQn6QkM
2. http://images.nvidia.com/content/technologies/deep-learning/...
3. https://images.nvidia.com/content/pdf/dgx1-v100-system-archi...
We kind of know what storage cost in a store and we know that Apple (Mac computers) and every phone manufacturer adds a ton of cost for a small increase. NVIDIA will probably do the same.
I have no idea what the cost for their cabling would be, but they exist in 100G, 200G, 400G and 800G speeds and you seem to need two of them.
We already know that it is going to be one single CPU and GPU and fixed memory. The GPU is most likely the RTX 5070 Ti laptop model (992 TFLOPS, clocked 1% higher to get 1 PFLOP).
I was an AI sceptic until 6 months ago, but that’s probably going to be my dev setup from spring onwards - running DeepSeek on it locally, with a nice RAG to pull in local documentation and datasheets, plus a curl plugin.
https://www.nvidia.com/en-us/project-digits/