Mac Studio with M3 Ultra and 512GB of unified memory review: opens up new workflows on a ~$10,000 desktop, like running a quantized version of DeepSeek R1 671B
if confusing — performance magic Federico Viticci / MacStories : The M3 Ultra Mac Studio for Local LLMs Brandon Hill / Tom's Hardware : Apple Mac Studio review: M3 Ultra offers amazing performance, silent operation, and a variety of ports, but no user memory or SSD upgrades, which are expensive Bluesky: Corey Quinn / @quinnypig.com : Maybe I'm spec-chasing, but with the M4 shipping now and the M5 due late this year, why would someone drop $10k on what's more or less last year's technology? [embedded post] Mastodon: Matt Birchler / @matt@isfeeling.social : Watching a few Mac Studio reviews, and man, local AI models are the new video export time when it comes to benchmarking high end computers these days. — I guess this is what you gotta do to see performance when the cheapest MacBook is outstanding at everything most people do already. X: @exolabs : The full DeepSeek R1 has 671B parameters @ 8-bit = 671GB. 2 x M3 Ultra 512GB Mac Studio with exo (connected with TB5) should be enough to run it with a long context. Alex Cheema / @alexocheema : Apple have given me early access to 2 maxed out M3 Ultra 512GB Mac Studios ahead of the public release. I will run the full DeepSeek R1 (8-bit) using @exolabs or die trying. The 1TB(!!) of Unified Memory should be enough for all 671B parameters + context. [image] Max Weinbach / @maxwinebach : This is from @Dave2D and shows the benefit of a MoE model for running on client! It's 37B active parameters, so even with 671B loaded into memory, it can generate at a usable 18.11 tok/s. Now there's a lot with KV Cache overhead and whatnot for context window but MLX handles it @pehdrew_ : DeepSeek R1 671B Q4 running on a Mac 3 Ultra! @Dave2D video! MoE are really performant if you have enough memory! @teortaxesTex thoughts? [image] Forums: r/LocalLLaMA : M3 Ultra 512GB does 18T/s with Deepseek R1 671B Q4 (DAVE2D REVIEW) r/apple : M3 Ultra Mac Studio Review
Context & Ripple Effects
Mac Studio began as Apple’s high-end desktop line with Ultra-class chips and large unified-memory configurations; the M3 Ultra extends that arc beyond the original Mac Studio’s 128GB memory ceiling.
The immediate relevance is less ordinary desktop speed than whether very large, quantized models become usable on a quiet local machine. That capability comes with the same constraint highlighted in the contemporaneous M3 Ultra review: memory and SSD capacity cannot be upgraded after purchase.
First-order effects
- A roughly $10,000, 512GB M3 Ultra Mac Studio can run a quantized DeepSeek R1 671B locally at about 18 tokens per second, giving developers and power users a desktop option for workflows that otherwise require smaller models or remote infrastructure.
- Buyers must commit to the memory and storage configuration upfront: Apple’s non-upgradeable design makes the 512GB configuration both the enabling feature and the irreversible cost decision.
Second-order effects
- Local-model users can weigh a high-capacity desktop against hosted inference on a workload-by-workload basis, particularly where model control or long-running local use matters more than the system’s upfront price.
- The result raises the value of software that can exploit quantization and mixture-of-experts models efficiently: the reported performance depends on a 671B-parameter model not activating all parameters for each token.
Third-order effects
- If similar systems continue to make large models practical on-device, AI compute will segment more sharply by memory capacity and memory bandwidth rather than by general-purpose desktop CPU performance alone.
- Apple’s tightly integrated hardware approach could make configuration choices a larger competitive lever, but fixed upgrades also leave buyers exposed when model memory requirements move faster than their purchased hardware.
The trend: This is one data point in the shift toward heterogeneous, memory-rich local AI hardware that can run increasingly capable models without relying entirely on the cloud.