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Nvidia DGX Spark

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Re: Nvidia DGX Spark

#21

Post by godelski »

Y_Y wrote: post #11

It's a bit disingenuous to claim 1 PFLOPs without making clear that's for FP4 (with "structured sparsity"?)

If you scroll down a little and see the chip icon, where it says "NVIDIA GB10 Superchip " it also says "Experience up to 1 petaFLOP of AI performance at FP4 precision with the NVIDIA Grace Blackwell architecture."

Further down, in the exploded view it says "Blackwell GPU 1PetaFLOP FP4 AI Compute"

Then further down in the spec chart they get less specific again with "Tensor Performance^1 1 PFLOP" and "^1" says "1 Theoretical FP4 TOPS using the sparsity feature."

Also, if you click "Reserve Now" the second line below that redundant "Reserve Now" button says "1 PFLOPS of FP4 AI performance"

I mean I'll give you that they could be more clear and that it's not cool to just hype up on FP4 performance, but they aren't exactly hiding the context like they did during GTC. I wouldn't call this "disingenuous"

Re: Nvidia DGX Spark

#22

Post by syntaxing »

While a completely different price point, I have a Jetson Orin Nano. Some people forget the kernels are more or less set in stone for product like these. I could rebuild my own Jetpack kernel but it’s not that straight forward to update something like CUDA or any other module. Unless you’re a business where your product relies on this hardware, I find it hard to buy this for consumer applications.

Re: Nvidia DGX Spark

#23

Post by [deleted] »

MBCook wrote: post #12

I’m not in this space, so I don’t know what’s normal, but I guess I’m a little surprised to see only 10 gig Ethernet for high speed connectivity. Yeah, it’s miles better than WiFi. But if there was something I’d think maybe benefit from Thunderbolt this would’ve been it. The ability to transfer large models or datasets that way just seems like it would be much faster and a real win for some customers.

[deleted]

Re: Nvidia DGX Spark

#24

Post by cherioo »

The mainstream options seem to be

Ryzen AI Max 395+, ~120 tops (fp8?), 128GB RAM, $1999

Nvidia DGX Spark, ~1000 tops fp4, 128GB RAM, $3999

Mac Studio max spec, ~120 tflops (fp16?), 512GB RAM, 3x bandwidth, $9499

DGX Spark appears to potentially offer the most token per second, but less useful/value as everyday pc.

Re: Nvidia DGX Spark

#25

Post by AlotOfReading »

Apes wrote: post #17
nightski wrote: post #6

Am I missing something or does the comparably priced (technically cheaper) Jetson Thor have double the PFLOPs of the Spark with the same memory capacity and similar bandwidth?

My understanding is the DGX Spark is optimized for training / fine tuning and the Jetson Thor is optimized for running inference. Architecturally, the DGX Spark has a far better cache setup to feed the GPU, and offers NVLINK support.

There's a lot of segmentation going on in the Blackwell generation from what I'm told.

Re: Nvidia DGX Spark

#26

Post by [deleted] »

cherioo wrote: post #24

The mainstream options seem to be Ryzen AI Max 395+, ~120 tops (fp8?), 128GB RAM, $1999 Nvidia DGX Spark, ~1000 tops fp4, 128GB RAM, $3999 Mac Studio max spec, ~120 tflops (fp16?), 512GB RAM, 3x bandwidth, $9499 DGX Spark appears to potentially offer the most token per second, but less useful/value as everyday pc.

[deleted]

Re: Nvidia DGX Spark

#27

Post by BoorishBears »

dirtyhand wrote: post #14

I was considering getting an RTX 5090 to run inference on some LLM models, but now I’m wondering if it’s worth paying an extra $2K for this option instead

No. These are practically useless for AI.

Their prompt processing speeds are absolutely abysmal: if you're trying to tinker from time to time, a GPU like a 5090 or renting GPUs is a much better option.

If you're just trying to prep for impending mainstream AI applications, few will be targeting this form factor: it's both too strong compared to mainstream hardware, and way too weak compared to dedicated AI-focused accelerators.

-

I'll admit I'm taking a less nuanced take than some would prefer, but I'm also trying to be direct: this is not ever going to be a better option than a 5090.

Re: Nvidia DGX Spark

#28

Post by aurareturn »

cherioo wrote: post #24

The mainstream options seem to be Ryzen AI Max 395+, ~120 tops (fp8?), 128GB RAM, $1999 Nvidia DGX Spark, ~1000 tops fp4, 128GB RAM, $3999 Mac Studio max spec, ~120 tflops (fp16?), 512GB RAM, 3x bandwidth, $9499 DGX Spark appears to potentially offer the most token per second, but less useful/value as everyday pc.

  Mac Studio max spec, ~120 tflops (fp16?), 384GB RAM, 3x bandwidth, $9499
512GB.

DGX has 256GB/s bandwidth so it wouldn't offer the most tokens/s.

Re: Nvidia DGX Spark

#29

Post by x2tyfi »

MBCook wrote: post #12

I’m not in this space, so I don’t know what’s normal, but I guess I’m a little surprised to see only 10 gig Ethernet for high speed connectivity. Yeah, it’s miles better than WiFi. But if there was something I’d think maybe benefit from Thunderbolt this would’ve been it. The ability to transfer large models or datasets that way just seems like it would be much faster and a real win for some customers.

You’re almost always going to bottleneck on your home internet or upstream ISP, rather than this local interface. That being said, you aren’t going to be waiting too long either way, depending on download speed. Deepseek R1 is 671GB. Multiply by 8 to get into bits: 5368Gb At full 10gbps (which, again, you probably won’t get): 5368Gb / 10gbps = 537 seconds to download 537s / 60 = 8.95 minutes. Call it 10m with overhead.
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