DGX Spark

You wont buy it https://www.microcenter.com/product/699008/nvidia-dgx-spark

Post on over to r/localllama or check out the wiki there for setups. DGX sparks are not an inference powerhouse for the $$$$ you’d spend. It’s not an enterprise grade system for 1500 users either. It’s for local, personal prototyping on the same environment as you’d deploy when you deploy to a large

but because it’s supposedly designed around AI workloads from the ground up But it's not. That's the wrong assumption. No matter how Nvidia spins it, DGX Spark is meant for prototyping and validating software that will be run on larger DGX-class or HGX-class clusters.

If you’re going for PURE text generation, there simply is nothing that beats the m3 ultra mac studio. Ask ANYONE who says that the gb10 is better to show it and do a direct comparison. The nvidia people like to say that the mac people are just exaggerating the numbers on paper - thats not true at al

At that price why buy it over the DGX Spark or one of the partner systems? You don't get access to the Nvidia software stack and no ConnectX-7 for clustering. The only benefit is the RYZEN AI Halo box can run Windows and seeing it's x86_64 it has uses outside of AI; but at $4k there are plenty of be

Training using the full Nvidia professional stack absolutely justifies it for a certain demographic. You're not it. You aren't going to train anything on a 5070ti with 256GB/s memory bandwidth. Also training is done with Fp16 so 128GB RAM limits you to about 60B parameters. People doing training use

IMHO, the DGX Spark isn't a good choice for a number of reasons: Its main selling point for consumers would be VRAM, but Comfy usually manages to run all of them on a 32GB card like the 5090, and usually on a 24GB card as well. Performance for the Spark is considerably slower than a 5090 The Spark e

Can you? "Compatibility has been another major frustration. Too many workflows still turn into time-consuming troubleshooting sessions involving unsupported paths, architecture-related issues, custom builds, patches, and repeated trial and error." https://forums.developer.nvidia.com/t/i-am-extremely