Best Mac for running local LLMs
A Mac shares one pool of memory between the CPU and GPU, so it can load models that would need several graphics cards. How fast it generates depends on the chip's memory bandwidth, which rises from the base chip through Pro and Max to Ultra. This page compares Macs on the same model at the same quant.
What is the best Mac for running LLMs locally?
Measured on Qwen3.8 27B at 4-bit, the fastest Mac people have reported is M5 Ultra 256GB, at 50.0 tokens per second (one run). M3 Ultra 512GB follows at 31.8 tokens per second (one run), then M5 Max 36GB at 25.8 tokens per second (one run). The table lists every Mac with a run of this model.
- 17-24 GBM4 24GBGen 6.5 t/s
- 25-32 GBM1 Max 32GBGen 15.8 t/s
- 33-96 GBM5 Max 36GBGen 25.8 t/s
- Over 96 GBM5 Ultra 256GBGen 50.0 t/s
Speed only matters if the model fits. Check what fits on your Mac.
Ranked on Qwen3.8 27B at 4-bit
Median tokens per second from plain runs: one Mac, one request, no speculative decoding, and the whole model in its memory. A run that kept part of the model in system RAM or on disk isn't ranked, because then the CPU and its memory set the pace as much as the Mac. The runs still differ in engine and context, and a long context can cut decode speed sharply, so each row shows what its median came from. Read a small gap between two rows as noise. Every Qwen3.8 report
| # | Mac | Memory | Gen t/s | PP t/s | Gen runs | Engine and context |
|---|---|---|---|---|---|---|
| 1 | M5 Ultra 256GB | 256 GBunified | 50.0 | 1800.01 run | 1 | oMLX8K context |
| 2 | M3 Ultra 512GB | 512 GBunified | 31.8 | 391.21 run | 1 | MLXcontext not stated |
| 3 | M5 Max 36GB | 36 GBunified | 25.8 | - | 1 | engine not statedcontext not stated |
| 4 | M1 Ultra 128GB | 128 GBunified | 25.1 | 153.31 run | 1 | engine not statedcontext not stated |
| 5 | M4 Max 64GB | 64 GBunified | 23.9 | 119.91 run | 1 | llama.cppcontext not stated |
| 6 | M4 Max 128GB | 128 GBunified | 21.8 | 236.61 run | 1 | llama.cppcontext not stated |
| 7 | M1 Max 64GB | 64 GBunified | 16.9 | - | 1 | LM Studio64K context |
| 8 | M1 Max 32GB | 32 GBunified | 15.8 | 81.81 run | 1 | MLXcontext not stated |
| 9 | M3 Ultra 256GB | 256 GBunified | 14.0 | 93.11 run | 1 | Ollamacontext not stated |
| 10 | M4 Pro 64GB | 64 GBunified | 12.9 | 126.24 runs | 4 | llama.cpp, 2 not statedcontext not stated |
| 11 | M2 Pro 32GB | 32 GBunified | 8.6 | 21.91 run | 1 | llama.cpp128K context |
| 12 | M4 24GB | 24 GBunified | 6.5 | 60.81 run | 1 | llama.cppcontext not stated |
No speed reports yet
These match the profile, but nobody has reported a plain run on them.
What to look for
Memory size decides what loads
macOS won't hand all of a Mac's memory to the GPU. By default llama.cpp, MLX and Ollama can use about two thirds of it on a 32 GB Mac or smaller and about three quarters from 36 GB up. The calculator grades every Mac against that limit and shows the setting that raises it.
The chip tier decides how fast it generates
Each step from base to Pro to Max to Ultra widens the memory bus, and generation speed follows. Two Macs with the same memory can differ a lot in speed, because some memory sizes ship with a cut-down chip on a narrower bus.
MLX or llama.cpp
MLX is Apple's own framework and is often the faster of the two on a Mac. llama.cpp supports more model formats and more tools build on it. Try your model in both before you decide a Mac is too slow for it.
Frequently asked
How much memory does a Mac need for local LLMs?
Enough for the model's weights, its context and macOS's share. The calculator's Mac tab takes your exact Mac and model and tells you which quant fits, so you don't have to work it out by hand.
Should I get an M-series Pro or Max for local LLMs?
The Max if generation speed matters to you. It has a wider memory bus than the Pro of the same generation, and generation speed tracks bandwidth closely. The table shows how far apart they are on the same model.
How is this list ranked?
Every Mac is ranked on its median speed for one reference model at one quant level, counting plain runs on a single machine only. The reference is the model the most Macs have reports of.