DGX Spark

NVIDIA did - they call it the DGX Spark. I have one. 128 GB of LPDDR5X memory to the Grace Blackwell chip. $3999 retail. Apple could smash this thing to bits with an M6 with a bajillion neural cores and the same RAM. They could price it at $1999 and take the whole market. They’re already doing it wi

Honestly it's fairly niche. It's meant for local LLM development/testing when working with large datasets (that's why it comes with 200Gb/s networking). But it's actually pretty damn bad at actually running LLMs (as in, you can load a large model but it's really slow), you want a beefier workstation

DGX is not for anything other than AI, and big models at that. It's a 5070ti speed wise. The 5070ti has 896GB/s memory bandwidth while the DGX Spark and the Minisforum S1 Max have 256GB/s memory bandwidth so it's literally 3.5x slower. You don't buy it for that. You buy it to run 200b parameter mode

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

Isn't this an apple to oranges comparison, though? Why use a tiny model when you can load up bigger model on the Ultra -- bigger even than a DGX Spark can handle. Qwen 3.5 had a 122B model available, so Qwen 3.6 may get on that sized soon. Qwen 3 coder-next is an 80B parameter model, despite a low a