Nvidia and cheap don't go together most of the time so I think they must have been very worried by developers and enthusiasts buying other hardware.
Consistent with their very developer focused strategy and probably a very smart idea since it's low enough spec to avoid canabalising sales of their existing products.
I suppose "cheapest" can be a very subjective term if the comparison is between things with different capabilities.
NVIDIA is a cheaper option than AMD only if you compare assume you want/need the fast networking NVIDIA are bundling with their system. According to the article the NIC would add $1500-$2000 to the price of another systems. I also failed to account for the extra memory bandwidth offered by M4 max. The apple system costs more but if you want/need that bandwidth then it's the cheapest of the three.
I guess "the system has a niche where it offers very good price/performance" is what I should have said. Not as snappy though.
the AMD system is 96GB max for the GPU. The 128GB allocates a minimum of 32GB to the CPU.
the Nvidia system is designed to be connected to a second if so desired, making it the cheapest way to get 256GB.
If you're just going for something under 96GB, haven't seen something cheaper than the AMD system for anything that can't fit on a traditional GPU. And even then, GPUs are obscene ripoff prices lately. Here's hoping these won't be scalped too.
That said, a nice OEM option if you need something for AI workloads where the GPU market is completely soaked with scalpers. Been considering a drive to California just so I can get a GPU directly at MicroCenter instead of paying scalper overheads.
If it's the memory bandwidth you are after the Mac Mini with the m4 pro has similar, but max 64GB ram.
This is key. Nvidia has a terrible reputation with long term support (as market leaders, they can easily afford that). Apple just now (last November) dropped OS updates for their 2014 boxes. While a Mac Studio 2025 will not be a ridiculous amount of compute power in 10 years, I fully expect Nvidia to completely abandon support and software updates for this in five years tops.
Hopefully, considering the interest it generated, I'd hope the Linux crowd will be able to carry it further, maybe with decent open-source drivers, way past the expiration date Nvidia sets.
In what space do they have this reputation? In drivers, I see they're supporting hardware that's 10 years old right now.
[edit] Oops, the Spark is $4,000, only the Ascent is at $3k now. Strix Halo systems vary from slightly slower (6%) to the same (on systems with LPDDR5x-8533, like the HP laptop).
> NVIDIA ConnectX-7 NIC these days often sells for $1500-2200 in single unit quantities, depending on the features and supply of the parts. At $2999 for a system with this buit-in that is awesome.
My naive analysis is: A high end Mac should be able to run each layer of an AI task about twice as fast because of the memory bandwidth. And the data going between layers is tiny enough to run over thunderbolt or even normal ethernet.
Is there an AI use case that prefers 250GB/s memory bandwidth plus 25GB/s interconnect over 500GB/s memory and 2GB/s interconnect? Are there other major use cases that prefer it?
For inference you can probably get by with 2GB/s assuming you can split the layers up nicely.
The interconnect can be a bottleneck for inference but only for networks with loads of activations and large batch sizes, or if you are doing tensor level parallelism.
So the bandwidth is dead even between AMD and Spark.