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

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

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

I own a M4 Max MacBook Pro 128GB RAM with a 4TB SSD. I recently bought a DGX Spark. Then returned it a few weeks later. I thought I was buying a smaller, slower Blackwell AI datacenter for my home lab when I bought a Spark. I was wrong. It’s a poorly-supported ARM knockoff of the sm120 ecosystem cal

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.

I can't tell if people are serious when they defend the reason for the DGX Spark existing. I honestly started laughing thinking you were joking about tough workloads training small models til you started comparing and adding defenses and I figured you are being serious... I'm not trying to be disres

Unfortunately, you dropped $4k for an inference server with anemic bandwidth (273 GB/s). To put that in perspective, that's 24 GB/s less than Apple's M5 Pro chip. If you run a dense model like gemma4:31b with that bandwidth, you're gonna have a bad time. You need an MoE model with a quant optimized

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