Apple just did something that AMD, Intel, and perhaps especially NVIDIA should take notice of. It launched new systems with updated chips and ton of unified memory. The new Apple Mac Studio M3 Ultra can be configured with up to 512GB of unified memory and there is also a M4 Max option. In our chats, Patrick has been asking for high-memory capacity SoCs for a few quarters now, and when I got sent this announcement today, I knew he already had one on order.
New 512GB Unified Memory Apple Mac Studio is the Local AI Play
Somewhat confusing is that Apple has been rolling out the M4 series for some time. We reviewed the Apple Mac Mini M4 quite some time ago. The Mac Studio is getting the M4 Max, but it is also getting the M3 Ultra. The M4 Max goes up to a 40-core GPU and 128GB of memory, which is going to be a great platform for folks. The M3 Ultra, however, will have 256GB and 512GB shared memory capacities and up to an 80-core GPU.
Of course, since this is apple, expect everything to be very pricey when it comes to options. Even with a paltry 1TB of storage, the 512GB model will set you back $9,499.
It is a shame that Apple still charges a lot on top of this for storage, but with Thunderbolt 5 and 10GbE, picking the $4600 16TB storage upgrade (we are not kidding there) is probably going to be one that many will skip in favor of network storage.
Final Words
I will let Patrick do the Final Words for this one. -Cliff
Editor’s Note: Apple is charging a huge amount for its systems at just under $10,000 (before tax) for a system configured with a modest 2TB of storage and 512GB of unified memory. At the same time, we have been running Deepseek-R1 671b on AMD EPYC systems for an article and video coming soon. The idea that you can run a big model like that on a system that is $10000 without having to get into the complexities of clustering should be a big deal to folks. Remember, a NVIDIA RTX 6000 Ada is $8999 (here is a B&H Affiliate link to the PNY version.) So for a few hundred dollars more, you get an entire system with the ability to do Thunderbolt 5 (120Gbps) clustering. That $8999 RTX 6000 Ada is a 2023-era part with only 48GB of memory, so Apple is offering a 10x memory footprint. One can, of course, point to the fact that the RTX 6000 Ada is a huge GPU, but the memory bit matters. We are going to talk about this a lot when we talk about running big AI models on EPYC soon, and in a very different context than others have previously.
I have told AMD’s server team they need a Strix Halo server part. Intel needs the same. NVIDIA has DIGITS. Still, Deepseek-R1 671b, or that class of models, running locally is great. We have a Framework Desktop with 128GB on order, and it is not a surprise the 128GB model is the most popular. The unified architectures for so long have been focused on lowering costs. As performance is ramping up in that segment, simply having access to a large memory pool becomes an almost binary yes/ no function to running higher-end workloads. The cost is high in the context of a workstation, but it is cheap when you compare it to NVIDIA’s cost per GB of memory.
I have a very strong feeling that the unified memory SoC model is going to be one that is going to be an enormous market in the future. For AMD and Intel, perhaps the challenge is that they have been looking at this market as a low-cost leadership segment for too long.
Some may find this a coincidence, but the March 17, 2025 date for the 512GB M3 Ultra model just happens to be the first day of NVIDIA GTC 2025.