DGX Spark

nvidia is being affected by the dram most likely. they dont make parts in house they actually buy them. if its short everywhere then nvidia cant buy their parts to make their new stuff they might sell the shovels but they are starting to get expensive to make. meanwhile apple jumped in to make custo

I went through the gauntlet. Started with an rtx 3090 + 128 gb ddr4 (sold) -> rtx 5090 + 128 gb ddr5 (kept) -> halo strix 395+ (returned) -> dgx spark (returned) -> m4 max 128 gb (kept) -> m3 ultra 256 gb (kept) If your main focus is coding there is nothing else than the m3 ultra or m4 max. The m5 m

Google One Pro: Storage, Gemini, etc.. Internet Home workers My servers & my ❤️ NVIDIA DGX Spark.

https://www.nvidia.com/en-us/products/workstations/dgx-spark/ it's $4,000. in my pc i have a 4070. The DGX spark is 30x more powerful for AI workloads

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

Welcome to the club. The harder thing to resist is buying a second one. forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10/dgx-spark-gb10/721 awesome resource Current top-pick for single spark: Qwen 3.5 122B-A10B, Qwen 3.6 27B (with MTP) or even the 35B-A3B if speed matters more. htt

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

I’ve got a Strix Halo and 2x GB10 (Nvidia DGX Spark, but the Asus version). For pure AI workloads, I’d go with the GB10. For example, on GPT-OSS-120B I’m hitting ~6000 pp and 50–60 t/s with vLLM, and I can easily serve 3 or 4 parallel requests while still outperforming my Strix Halo, which struggles

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

Those models perform a lot worse than the API you'd spend 100k/year on. And the inference speed is a single digit percent of it as well, uselessly slow. If you consider those aspects this is not a great purchase. Honestly better off just getting Nvidia's 100GB workstation GPUs for 7k a piece. Their