The best Mac for local LLMs is the one with enough unified memory to hold your model and enough bandwidth to read it quickly. Apple prices those two things very differently across the Mac mini, Mac Studio and MacBook Pro, so you can overpay badly by picking on chip name alone. A 128GB Mac Studio costs less per gigabyte than a 48GB MacBook Pro, and a $1,099 Mac mini generates tokens faster on an 8B model than most people can read them.
We ranked every Apple Silicon config that makes sense for local models by two numbers: dollars per GB of unified memory and tokens per second by model size. The tok/s figures come from the llama.cpp Apple Silicon benchmark table and published MLX runs, because there is no Mac in our lab, and we say plainly which rows are measured and which are scaled from bandwidth. If you’re still deciding between a Mac and a PC, our Mac mini vs mini PC vs GPU comparison covers that choice first. Prices and stock below were checked on October 27, 2026.
Quick picks: best Mac for local LLMs by model size
Pick by the largest model you want to run every day, not by the chip name, because memory decides what loads and bandwidth decides how fast it answers:
- Best overall, up to 120B: Mac Studio M5 Max, 40-core GPU, 128GB. About $5,100 from Apple, the lowest price per GB of any 128GB Mac, and the same chip runs gpt-oss-120b at about 88 tok/s.
- Best value, 32B to 70B: Mac mini M5 Pro, 64GB. About $2,700 from Apple. It’s the cheapest Mac that fits a 70B model at 4-bit, though only at about 6 tok/s.
- Best budget, 8B to 20B: Mac mini M6, 24GB. About $1,100 on Amazon. It loads gpt-oss-20b and Qwen3 14B and runs 8B models at an estimated 28 tok/s.
- Best laptop: MacBook Pro 14-inch M5 Max, 128GB. About $6,700. The same chip and memory as the Studio pick, for $1,600 more (with a 2TB SSD) and a battery. Check what Apple launches on October 28 before you order.
- Biggest models: Mac Studio M5 Ultra, 256GB. About $9,500. It’s the only Mac that holds Qwen3 235B, and its 1.2TB/s bus doubles the M5 Max’s.
Notice there is no MacBook Pro M5 Pro and no 36GB Mac Studio on that list. Both cost more per gigabyte than a Mac mini with the same or better memory, which the table further down shows in dollars.
How we picked these Macs
We do not own an M5-generation Mac, so this guide does not claim lab benchmarks. What we did instead was pull every number from a source you can check and do the arithmetic in the open:
- Prices: read from Apple’s US store configurators and from Amazon’s catalog API on October 27. Amazon links go only to new units in stock.
- Specs: memory options, bandwidth and core counts come from Apple’s spec pages for the Mac Studio, Mac mini and MacBook Pro.
- Measured speed: the llama.cpp Apple Silicon table (Llama 2 7B, Metal) and a published MLX run on a 128GB M5 Max covering gpt-oss-120b and Qwen3.5 models.
- Estimated speed: dense models from 8B to 70B, scaled from each chip’s measured 7B result by file size. Those cells are marked as estimates.
We left out the MacBook Air and the base M5 MacBook Pro. Both top out at 32GB with 153GB/s or less, so a Mac mini M6 at 32GB does the same work for less money, and a desktop doesn’t throttle under a long generation run. Renewed and used listings were also excluded, since their memory configs on Amazon are often mislabelled.
Unified memory per dollar on every Mac
The Mac Studio M5 Ultra at 256GB is the cheapest Mac per gigabyte at $37.11, followed by the 128GB M5 Max Studio at $39.84. That surprises people who assume the mini is the value play, but Apple’s memory upgrades get cheaper per GB as you go up the range, while the laptop premium never goes away. These are Apple list prices with the lowest SSD each config allows:
| Config | Apple price | Memory | Bandwidth | $ per GB | GB/s per $1,000 |
|---|---|---|---|---|---|
| Mac mini M6, 16GB/256GB | $899 | 16GB | 153 GB/s | $56.19 | 170 |
| Mac mini M6, 24GB/256GB | $1,099 | 24GB | 170 GB/s | $45.79 | 155 |
| Mac mini M6, 32GB/256GB | $1,299 | 32GB | 170 GB/s | $40.59 | 131 |
| Mac mini M5 Pro, 48GB/512GB | $2,299 | 48GB | 307 GB/s | $47.90 | 134 |
| Mac mini M5 Pro, 64GB/512GB | $2,699 | 64GB | 307 GB/s | $42.17 | 114 |
| Mac Studio M5 Max 32-core GPU, 36GB | $2,499 | 36GB | 460 GB/s | $69.42 | 184 |
| Mac Studio M5 Max 40-core GPU, 128GB | $5,099 | 128GB | 614 GB/s | $39.84 | 120 |
| Mac Studio M5 Ultra, 96GB/1TB | $5,499 | 96GB | 1.2 TB/s | $57.28 | 218 |
| Mac Studio M5 Ultra, 256GB/1TB | $9,499 | 256GB | 1.2 TB/s | $37.11 | 126 |
| MacBook Pro 14 M5 Pro, 48GB/1TB | $3,099 | 48GB | 307 GB/s | $64.56 | 99 |
| MacBook Pro 14 M5 Pro, 64GB/1TB | $3,699 | 64GB | 307 GB/s | $57.80 | 83 |
| MacBook Pro 14 M5 Max, 128GB/2TB | $6,699 | 128GB | 614 GB/s | $52.34 | 92 |
| MacBook Pro 16 M5 Max, 128GB/2TB | $6,999 | 128GB | 614 GB/s | $54.68 | 88 |
Read the two right-hand columns together before you pick. The 36GB Mac Studio and the 96GB Ultra score highest on bandwidth per dollar, but they pair a fast bus with too little memory for the models that bus is fast enough for, so they’re traps for LLM work.
The 64GB MacBook Pro has the worst bandwidth per dollar on the list. It needs the 18-core M5 Pro, and against a Mac mini with that same chip and 64GB ($2,899) you pay $800 more, for a screen, a battery and a 1TB SSD instead of 512GB. If you do not need to carry the model around, buy the desktop.
Mac tokens per second by model size
The speed you see on a Mac tracks memory bandwidth almost linearly, because every new token reads the whole set of active weights once. The llama.cpp table makes that visible: on Llama 2 7B at Q4_0, the M6 at 170GB/s generates 35.7 tok/s, the M5 Pro 67.6, the 40-core M5 Max 119.9 and the 80-core M5 Ultra 179.1. Prompt processing is a different story, since it depends on GPU cores, and there the Ultra (4,945 tok/s) is five times the M6 row (989 tok/s). That M6 row was run on an 8-core GPU part. The Mac mini’s M6 has 12 GPU cores, so expect its prompt speed to land somewhat higher. Generation follows bandwidth and should match.
For bigger dense models, we took each chip’s effective bandwidth from that 7B run (tok/s times the 3.82GB file) and divided it by the model’s file size. The measured MLX rows come from a 14-inch MacBook Pro with the 40-core M5 Max and 128GB, published by Hardware Corner, at a 4K context:
| Model (file size) | M6 | M5 Pro | M5 Max 40-core | M5 Ultra 80-core |
|---|---|---|---|---|
| Llama 2 7B Q4_0 (3.8GB), measured | 35.7 | 67.6 | 119.9 | 179.1 |
| Llama 3.1 8B Q4_K_M (4.9GB), est. | ~28 | ~52 | ~93 | ~139 |
| Qwen3 14B Q4 (9.3GB), est. | ~15 | ~28 | ~49 | ~74 |
| Qwen3 32B Q4_K_M (19.8GB), est. | ~7 (32GB only) | ~13 | ~23 | ~35 |
| Llama 3.3 70B Q4_K_M (42.5GB), est. | does not fit | ~6 (64GB only) | ~11 | ~16 |
| Qwen3.5-27B 6-bit MLX (26GB), measured | does not fit | no data | 23.6 | no data |
| gpt-oss-120b MLX (64GB), measured | does not fit | does not fit | 87.9 | no data |
| Qwen3.5-122B-A10B 4-bit MLX (69.6GB), measured | does not fit | does not fit | 65.9 | no data |
Use the table to choose what you run, not only what you buy:
- Mixture-of-experts models are the Mac’s best case: gpt-oss-120b runs about eight times faster than our dense 70B estimate on the same M5 Max, because each token reads only the active experts. On a Mac, the right 120B model can feel quicker than a dense 32B one.
- Dense 70B is where cheaper Macs give up: 6 tok/s on an M5 Pro is fine for batch jobs and painful for chat. If a dense 70B is your daily driver, you need a Max or an Ultra.
- Small models don’t need a big chip: every Mac here runs an 8B model faster than you can read, so don’t pay for a Max to run 8B.
Context length costs speed as well. The same M5 Max run drops from 87.9 tok/s at 4K to 64.5 tok/s at 32K on gpt-oss-120b, and Qwen3.5-27B falls from 23.6 to 14.9. Plan on a quarter to 40 percent less speed once you feed your model long documents or a big codebase.
Treat the scaled numbers above as estimates, because real runs differ with the runtime you pick (MLX, llama.cpp or Ollama) and its version. Our Ollama vs vLLM vs llama.cpp breakdown explains where those runtimes differ.
1. Mac Studio M5 Max (40-core GPU, 128GB)
The 128GB Mac Studio wins for most people who want to run serious models locally, because it’s the cheapest way to get 128GB behind a 614GB/s bus. At $5,099 with a 512GB SSD it costs $39.84 per GB, $1,600 less than the same chip and memory in a 14-inch MacBook Pro (which does come with a 2TB SSD), and it holds gpt-oss-120b with room left for a long context.
You get the strongest measured numbers in the guide on this chip: 87.9 tok/s on gpt-oss-120b, 65.9 on Qwen3.5-122B at 4-bit and 79.3 on Qwen3-Coder-Next at 8-bit, which peaked at 92.6GB of memory with a 64K context. That last run shows why 128GB matters more than it sounds. A 64GB machine cannot load an 85GB coding model at all, and a 96GB one can’t hold it either unless you raise macOS’s GPU memory limit by hand.
- Who it is for: anyone who wants 70B to 120B class models at chat speed on one quiet box, or a small team sharing one local endpoint.
- Skip it if: your models stay under 30B. A Mac mini M5 Pro does that for half the price.
- Watch the config: 128GB needs the 40-core GPU. The $2,499 base Studio has the 32-core GPU and only 36GB, and it can’t be upgraded later.
Amazon does not stock the 128GB build you want. The only Studios there are the 36GB base model at about $2,300 sold by Amazon and a 48GB 40-core build with a 1TB SSD from a reseller at about $3,400, the same as Apple’s price for that config. Neither is a local LLM machine you’d want. Order the 128GB config from Apple directly and check the live price there.
2. Mac mini M5 Pro (64GB)
The 64GB Mac mini M5 Pro is the best value for 32B models, and it’s the cheapest Mac that loads a dense 70B at 4-bit. Apple sells it for $2,699 with the 15-core CPU, which works out to $42.17 per GB, and the 307GB/s bus runs Qwen3 32B at an estimated 13 tok/s.
Amazon has no 64GB M5 Pro mini. The in-stock unit there is the 18-core 48GB build with a 1TB SSD at about $2,799, sold by an Apple Premier Partner at Apple’s own price for that config. It’s a fine 32B machine, but it will not fit a 70B model with any context, so if 70B is on your list, the 64GB build from Apple is $100 cheaper and the better buy.
- Who it’s for: developers running a 14B to 32B coding or chat model all day, plus agents that call it.
- Skip it if: you want 70B for chat. About 6 tok/s works for overnight batch jobs, not for a conversation.
- Plan storage now: the SSD is soldered, and the 512GB base fills fast with model files, so put your model library on a Thunderbolt 5 NVMe enclosure in a rear port.
Skip the 18-core M5 Pro upgrade unless you process long prompts. It adds $200 and does not change token generation: the llama.cpp table shows the 20-core GPU at 66.3 tok/s against 67.6 for the 16-core part. It helps prompt processing by about 20 percent. That only matters if you feed it long documents. Our Mac mini guide for AI agents covers the storage and always-on setup for this machine.
3. Mac mini M6 (24GB)
For 8B to 20B models, the 24GB Mac mini M6 is the cheapest Mac worth buying. At $1,099 it is 170GB/s and 24GB, which loads gpt-oss-20b (a 14GB download) and Qwen3 14B with room for context, and runs an 8B model at an estimated 28 tok/s.
Skip the 16GB base model for LLM work. It costs $200 less, but Apple wires it to a slower 153GB/s bus, and after macOS takes its share there isn’t room for anything bigger than a 14B model at 4-bit. The 24GB unit on Amazon is a factory-upgraded build with a 256GB SSD at about $1,099, the same as Apple’s price, sold by an Apple Premier Partner. Check the live price before you order.
- Who it’s for: a first local LLM box, a private assistant on an 8B model, or a home server that runs one small model next to everything else.
- Skip it if: you already know you want 32B models. The 32GB M6 at $1,299 just fits them at about 7 tok/s, and the M5 Pro is twice as fast.
- Know the ports: the M6’s rear ports are Thunderbolt 4 at 40Gb/s, so a fast external SSD runs at about half the speed it would on an M5 Pro.
4. MacBook Pro 14-inch M5 Max (128GB)
If the model has to travel with you, the 14-inch MacBook Pro with the 40-core M5 Max and 128GB is the laptop to buy. It’s the exact machine behind the measured MLX numbers above, so you can expect about 88 tok/s on gpt-oss-120b and 66 tok/s on Qwen3.5-122B away from your desk.
You pay for the portability. At $6,699 with the 2TB SSD that every M5 Max MacBook Pro starts with, it costs $52.34 per GB against $39.84 for the same chip in a Mac Studio. Amazon has the Space Black build in stock at about $6,699 from an Apple Premier Partner, the same as Apple’s price; check the live price, because these high-memory configs sell out often.
- Who it’s for: consultants, researchers and developers who need a 120B model offline, on a plane or on a client site.
- Skip it if: the laptop mostly sits on a desk. A Mac Studio is $1,600 cheaper for the same chip and memory, though with a 512GB SSD instead of 2TB.
- Avoid the 32-core M5 Max: it is fixed at 36GB and drops the bus to 460GB/s, which is the worst pairing for LLMs in the lineup.
If you want the bigger screen, the 16-inch adds $300 for the same 128GB config. Our best laptop for local LLMs round-up puts this machine against the Strix Halo and RTX laptops if you’re not set on macOS.
5. Mac Studio M5 Ultra (256GB)
The 256GB M5 Ultra is the only Mac you can order today that loads Qwen3 235B, which is a 142GB download in Ollama. At $9,499 with the 64-core GPU and a 1TB SSD it is also the cheapest Mac per gigabyte, at $37.11, and its 1.2TB/s bus is twice the M5 Max’s.
Only the 80-core Ultra appears in the llama.cpp table, at 179.1 tok/s on the 7B test, so the dense estimates above assume that chip. The 64-core part shares the same 1.2TB/s bus, which suggests similar generation speed, but nobody has published a run on it yet. The 80-core upgrade costs $1,300 ($10,799 at 256GB), and since its extra GPU cores mostly speed up prompt processing, buy it only if you work with long contexts.
- Who it’s for: people who need frontier-size open models such as Qwen3 235B on one machine, or the fastest dense 70B on a Mac at about 16 tok/s.
- Skip the 96GB Ultra: at $5,499 it has less memory than the $5,099 M5 Max at 128GB, so the 85GB coding model that the cheaper machine runs won’t fit in its default GPU memory limit.
- Wait if you need 512GB: Apple’s store still lists a 512GB Ultra option as coming late October, with no price yet, and its spec page ties 512GB to the 80-core Ultra, so budget from the $10,799 256GB build upward. It would fit Qwen3-Coder 480B (290GB); DeepSeek R1 671B at 4-bit (404GB) would need the GPU memory limit raised by hand.
Amazon only carries the 96GB Ultra, and a reseller lists it at about $6,399, which is $900 over Apple’s price for a config we’d skip anyway. Buy the 256GB build from Apple.
Should you wait for the M6 MacBook Pro?
Not if you’re buying a desktop, but laptop buyers should wait a day or two. As of the morning of October 27, Apple hadn’t announced an M6 MacBook Pro: its Mac pages still list M5, M5 Pro and M5 Max only. MacRumors reported that an entry 14-inch MacBook Pro with the M6 from the Mac mini was expected that day, and higher-end 14-inch and 16-inch models on October 28.
None of that changes your choice much. The M6 in the Mac mini tops out at 32GB and 170GB/s, so an M6 MacBook Pro built on that chip would land in the budget tier above, at laptop prices.
What would change the laptop pick is a higher-end chip in those models with more bandwidth or more than 128GB of memory. Don’t spend $6,700 on an M5 Max MacBook Pro the day before that launch. Until Apple publishes those specs, the 128GB M5 Max is the most memory you can get in a Mac laptop, and the Mac Studio still beats any laptop on price per GB.
How much memory does a Mac need for local LLMs?
Take the model’s file size and add room for the context. Then check that the total sits well under the GPU’s share of unified memory, not under the full amount you paid for.
macOS limits how much of your memory Metal can wire for the GPU by default. llama.cpp prints that limit as recommendedMaxWorkingSetSize when it loads, and one commenter in this long-running llama.cpp discussion puts it at about two thirds to three quarters of RAM. The iogpu.wired_limit_mb sysctl raises it, but the setting resets on reboot and leaves less memory for macOS.
Using that three-quarter rule of thumb and Ollama’s download sizes, this is what each memory tier holds comfortably:
- 24GB: 8B and 14B models (5.2GB and 9.3GB), and gpt-oss-20b (14GB) with a modest context.
- 32GB: Qwen3 30B-A3B and Qwen3 32B at about 19-20GB, with little room for long context.
- 48GB: 32B models with long context, but not a 43GB 70B model.
- 64GB: Llama 3.3 70B at 4-bit (43GB) with a short context.
- 128GB: gpt-oss-120b (65GB) and 120B-class MoE models with a long context.
- 256GB: Qwen3 235B (142GB).
Buy one tier above the model you run today. Memory is fixed at purchase on every Apple Silicon Mac, so the extra $200 to $1,000 now is cheaper than replacing the whole machine when your next model doesn’t fit. Our guide on how much VRAM an LLM needs shows the KV cache maths if you want to size context precisely.
Bandwidth is the second filter, and it is where chip names mislead. A 36GB M5 Max and a 128GB M5 Max are both “M5 Max”, yet one has 460GB/s and the other 614GB/s. Within the same memory size, always take the faster bus: it’s the only spec that raises tokens per second on every model.
When a Mac is the wrong answer
A Mac wins on price per GB at 128GB and up and on running quietly as one box, but three kinds of buyer should look elsewhere:
- You need 128GB for under $4,000: a Ryzen AI Max+ 395 mini PC gives you 128GB for about $3,600 on Amazon (check the live price), roughly 70 percent of the Mac Studio’s price, though at 256GB/s. Our Ryzen AI Max+ 395 mini PC comparison ranks the boxes.
- You serve many users or fine-tune: prompt processing and batched serving are where NVIDIA wins. On gpt-oss-120b an RTX Pro 6000 processes prompts about five times faster than the M5 Max (6,512 vs 1,325 tok/s at 4K), and CUDA is what training tools expect.
- Your models fit in 24GB: a used RTX 3090 or a new GPU in a PC you already own generates faster than any Mac mini on small models.
If you run one model for yourself, the Mac is the simplest box that does it quietly. If you’re weighing the three 128GB options head to head, the DGX Spark vs Strix Halo vs Mac Studio comparison has the price per GB and tok/s side by side, and the best mini PC for local LLMs list covers the non-Apple boxes from 32GB up.
