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

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

Depends on what you call "professional grade" The 400 billion model you just need enough unified ram or vram and some decent flops and bandwith. e.g. you can run the 671b deepseek model at 18 tok/sec on a mac studio m3 ultra with 512 gb of unified ram. Price is around 10k I think. No need for a gian

You need an NVIDIA DGX Spark running local models. The one time purchasing cost is less than your estimated monthly subscription cost, and the device should be good for at least 3 years.

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

Best a Mac mini can do is 64GB. They can only use a Pro M SOC. To get a 128GB M4 Max SOC you need at least a Mac Studio (or a MacBook Pro). At that point it is hard to say no to the Nvidia DGX Spark @ $5k. That said wait until March 4th to see what new goodies Apple drops. I read rumors there could

Long-term, it's not, and it cannot be. As it stands, the Nvidia DGX Spark systems, which would allow you to run the equivalent of ChatGPT 5.x on your desktop, are available for <$5000. That might seem absurdly expensive (and it is), but in a few years, that price will be down to <$2500. And in a dec