On Apple Silicon, unified memory sets how big a model you can run, but macOS only lets the GPU use part of it. Here's which Mac to buy for today's models, from a 32GB Mac mini to a 128GB Mac Studio.
It comes down to one number: how much unified memory it has. On Apple Silicon the CPU and GPU share one pool, so the RAM figure is what sets how large a model you can load. But macOS doesn't hand all of it to the GPU. For most people a Mac mini with 32GB to 48GB is the sweet spot for today's 27B to 35B models. If you want the 100B-plus open models, a Mac Studio with 128GB is the machine. Here's which to buy, with fits worked out on our VRAM engine against the memory the GPU can actually use.
Why unified memory makes Macs good for AI, and the catch
On a PC, a model has to fit the graphics card's dedicated VRAM: a 24GB card has 24GB, full stop. On a Mac, the GPU draws from the same memory as everything else, so a 64GB Mac can hold models that would need two or more 24GB GPUs on a PC, in a small, quiet box.
The catch is that a 32GB Mac is not a 32GB graphics card. macOS sets a recommended GPU memory ceiling, which Apple's Metal documentation calls recommendedMaxWorkingSetSize, and going past it hurts performance. Apple doesn't publish the percentage. The values llama.cpp users have logged run from about two-thirds on 16GB to 32GB Macs to three-quarters or more on bigger ones, so we plan on two-thirds at 32GB and below and three-quarters above. That gives a 32GB Mac about 21.3GiB for the model and a 128GB Mac about 96GiB. The full method is in our Mac mini buying guide.
The other catch is speed. Generation speed is set mostly by memory bandwidth, and per Apple's spec pages the base Mac mini chips run at 153 to 170 GB/s, the M5 Pro at 307 GB/s, the M5 Max at up to 614 GB/s and the M5 Ultra at about 1.2 TB/s. A used RTX 3090 has 936 GB/s. So a Mac is about capacity and efficiency, not peak speed.
What fits each Mac memory size (our VRAM engine, 8K context, Oct 28, 2026)
16GB (base Mac mini)
Memory
~10.7GiB
GPU budget (our planning figure)
Qwen3.5 9B at Q6_K to Q8_0, Gemma 4 12B at Q6_K (tight)
24GB
Memory
~16GiB
GPU budget (our planning figure)
Gemma 4 12B at Q8_0; gpt-oss-20b at Q4_K_M (Q5_K_M tight)
32GB
Memory
~21.3GiB
GPU budget (our planning figure)
Qwen3.6 27B at Q5_K_M (tight); Qwen3.6 35B-A3B at Q4_K_M (tight)
48GB (Mac mini M5 Pro)
Memory
~36GiB
GPU budget (our planning figure)
Qwen3.6 27B at Q8_0; Qwen3.6 35B-A3B at Q6_K; Gemma 4 31B at Q6_K or Q8_0 (tight)
64GB
Memory
~48GiB
GPU budget (our planning figure)
The 27B to 35B models at Q8_0; Qwen3-Next 80B at Q4_K_M (tight)
128GB (Mac Studio)
Memory
~96GiB
GPU budget (our planning figure)
gpt-oss-120b at Q6_K (tight); Qwen3.5 122B-A10B at Q5_K_M; Qwen3-Next 80B at Q8_0
256GB (Mac Studio M5 Ultra)
Memory
~192GiB
GPU budget (our planning figure)
Qwen3 235B-A22B at Q6_K (tight); gpt-oss-120b at Q8_0
Memory
GPU budget (our planning figure)
What fits at 8K context
16GB (base Mac mini)
~10.7GiB
Qwen3.5 9B at Q6_K to Q8_0, Gemma 4 12B at Q6_K (tight)
24GB
~16GiB
Gemma 4 12B at Q8_0; gpt-oss-20b at Q4_K_M (Q5_K_M tight)
32GB
~21.3GiB
Qwen3.6 27B at Q5_K_M (tight); Qwen3.6 35B-A3B at Q4_K_M (tight)
48GB (Mac mini M5 Pro)
~36GiB
Qwen3.6 27B at Q8_0; Qwen3.6 35B-A3B at Q6_K; Gemma 4 31B at Q6_K or Q8_0 (tight)
64GB
~48GiB
The 27B to 35B models at Q8_0; Qwen3-Next 80B at Q4_K_M (tight)
128GB (Mac Studio)
~96GiB
gpt-oss-120b at Q6_K (tight); Qwen3.5 122B-A10B at Q5_K_M; Qwen3-Next 80B at Q8_0
256GB (Mac Studio M5 Ultra)
~192GiB
Qwen3 235B-A22B at Q6_K (tight); gpt-oss-120b at Q8_0
"Tight" means the model uses more than 90% of the GPU budget. Going from 8K to 32K context changes little for the hybrid-attention models (Qwen3.5 and 3.6, Gemma 4), because only a few of their layers keep a growing KV cache: Qwen3.6 27B on a 32GB Mac stays at Q5_K_M (about 21.2GiB) at 32K. Per-model detail: Qwen3.6 27B, Qwen3.6 35B-A3B, gpt-oss-120b.
On a Mac, unified memory is your VRAM, but plan on the share macOS gives the GPU, not the full RAM figure. · Unsplash
Which Mac should you buy?
Match the memory to the biggest model you're sure you'll run, then check the next size up. Unified memory can't be upgraded later, so the memory you pick at checkout is the biggest model that Mac will ever hold.
For small models only (9B to 12B), a 24GB Mac mini is enough. For the 27B to 35B models that are now the sweet spot for serious local use, 32GB loads them at 4-bit but tight, and 48GB on the M5 Pro Mac mini runs them at Q6_K to Q8_0 with room to spare, plus about 1.8 times the bandwidth of the base chip. For 80B to 120B models, skip the Mac mini and look at a Mac Studio with 128GB. Only the 256GB Mac Studio holds a 235B model. On October 4, 2026, Apple's US store listed the 32GB Mac mini at $1,299, the 48GB Mac mini M5 Pro at $2,299 and the 128GB Mac Studio M5 Max at $5,099; check the store for current prices. Our Mac mini buying guide lists every config. Ollama and LM Studio both run on Apple Silicon, so setup is easy either way. Compare against a PC build before deciding.
8/ 10
Verdict
Best Mac for local AI (October 2026)
A 48GB Mac mini M5 Pro is the value pick for today's 27B to 35B models, which it runs at Q6_K to Q8_0 on our numbers. 32GB loads them at 4-bit if budget is tight. For 100B-plus models, a 128GB Mac Studio. Plan on the GPU memory budget, not the full RAM figure.
Best for: Anyone wanting to run large local models quietly and efficiently, who values memory capacity over peak speed.
Quick answers
What is the best Mac for running AI locally?
For most people, a Mac mini with 48GB (M5 Pro). On our VRAM numbers it runs Qwen3.6 27B at Q8_0 and Qwen3.6 35B-A3B at Q6_K with room to spare. A 32GB Mac mini loads the same models at 4-bit, but tight. For 100B-plus models such as gpt-oss-120b, a Mac Studio with 128GB.
How much of a Mac's memory can a local AI model use?
Not all of it. macOS sets a recommended GPU memory ceiling that Apple doesn't publish. Values logged by llama.cpp users run from about two-thirds on 16GB to 32GB Macs to three-quarters or more on bigger ones, so we plan on that range. A 32GB Mac gives the model about 21GiB.
How much memory do I need on a Mac for local AI?
24GB for 9B to 12B models and gpt-oss-20b; 32GB to 48GB for the 27B to 35B models; 128GB for 80B to 120B models; 256GB for a 235B model. Memory can't be upgraded later, so buy for the biggest model you expect to run within a year.
Is a Mac faster than an NVIDIA GPU for local AI?
Usually not on models that fit both. Generation speed follows memory bandwidth, and only the Mac Studio's M5 Ultra (about 1.2 TB/s per Apple) beats a used RTX 3090's 936 GB/s. The Mac's advantage is how much memory you get, and power draw.
