Our time with a 128GB M4 Max Mac Studio demonstrates why these systems have been so popular among local AI trailblazers. For starters, the spec we tested has an incredibly potent 16-core CPU that’s leagues ahead of both Nvidia’s GB10 and AMD’s Ryzen AI Max+ 395 in both single-threaded and multi-threaded performance.
The massive memory bandwidth advantage of the 40-core GPU configuration of the M4 Max versus Strix Halo and GB10—546 GB/s vs 273 GB/s—also translates directly to higher tokens-per-second throughput in local LLM workloads. The M4 Max’s LLM inference performance is by far the highest of any unified memory platform we’ve tested.
But our tests demonstrate that there’s much more to delivered performance than that raw bandwidth figure. Depending on the model you choose, you might see the M4 Max generate as few as 27% more tokens per second or as many as 82% more TPS than an Nvidia GB10 box, at least with the llama.cpp inference engine.
This result emphasizes that you can’t just eyeball memory bandwidth and conclude that twice the bandwidth means twice as many tokens per second for LLM inference. Testing actual workloads remains essential.
And the Mac Studio doesn’t totally wipe the floor with the competition in our tests. For work like image generation, the Nvidia GB10 GPU still holds a large advantage over the M4 Max. Apple claims this workload will perform significantly better on the most recent M5 Max with its latest GPU architecture.
Beyond its impressive raw performance with LLMs and in general processing tasks, the Mac Studio is a master class in industrial design. It looks distinctly classy on a desk, and its cooling design keeps it incredibly quiet. Under load, it’s barely louder than the Nvidia GB10-powered Dell Pro Max and Ryzen AI Halo are at idle. And when the Studio itself is idling, it’s practically silent. This level of performance in such a compact, quiet, and refined chassis is incredibly impressive.
There’s one major asterisk, though: the ongoing RAMpocalypse means that even if you do want to use a Mac Studio as a foundation for your local LLMs, these systems are no longer available in the configuration that we tested.
A Studio with the high-end M4 Max as seen in this review currently tops out at 64GB of RAM for $5299, and the M3 Ultra version tops out at 96GB (with an even higher 819GB/s of memory bandwidth) for the same price. But neither of these configurations are available from stock, and lead times for both are about two months out, meaning that you practically can’t buy a Mac Studio new right now.
If you really want to try out local AI on Apple Silicon with a large memory pool to match, the best current option may be a MacBook Pro with an M5 Max inside, which is still readily available from stock, still allows you to get up to 128GB of RAM, and benefits from Apple’s latest GPU architecture with dedicated Neural Accelerators for matrix math operations.
But depending on your SSD choice, a 128GB M5 Max MacBook Pro with the highest-end 16-core CPU and 40-core GPU is still a $7K to $10K system today. Even in today’s world of rising prices for access to frontier models, that’s a lot of tokens from the cloud – and, almost assuredly, more intelligent ones – than anything you can get from a single 128GB system from any vendor.
But if you’re dead-set on a single Apple Silicon system with the most memory possible, and you want it now in order to ride the local AI wave, a maxed-out M5 Max MacBook Pro is your only ticket at the moment. Given the refinement and flexibility of the Mac Studio as a desktop or server, we can only hope Apple brings that chip to this system, too.
