Guides · Mac · 8 min read
The best local LLMs for Mac in 2026: M1 to M4, by memory
Apple Silicon Macs are some of the easiest machines for running AI models locally. The CPU and GPU share one pool of fast memory, so a Mac with 48 GB can load models that would need two graphics cards on a PC. This guide lists the best models for each memory size, from an 8 GB MacBook Air to a 256 GB Mac Studio, and explains what the chip changes.
Two numbers decide everything
Unified memory decides what fits. macOS lets the GPU use roughly 70 to 75% of unified memory by default; the rest stays with the system. We use 72% in our checker, so a 16 GB Mac has about 11.5 GB for a model and a 48 GB Mac about 34.6 GB.
The chip decides how fast it runs. What matters is memory bandwidth, and it grows with each tier:
| Chip tier | Bandwidth | Speed vs base chip |
|---|---|---|
| M1 / M2 / M3 / M4 | 68 to 120 GB/s | 1x |
| M1 Pro to M4 Pro | 150 to 273 GB/s | about 2x |
| M1 Max to M4 Max | 400 to 546 GB/s | about 4x |
| M1 Ultra to M3 Ultra | 800 to 819 GB/s | about 7x |
So an M4 Max runs the same model roughly four times faster than a base M4, as long as both have enough memory to load it.
8 GB: small models only
With about 5.8 GB for the model, an 8 GB Mac is limited to small models. Gemma 3 4B and Qwen3.5 4B run comfortably at 11 to 14 tokens per second on an M1. 7B and 8B models such as Qwen3 8B only fit at a low Q3 quantization, which costs some quality. Fine for quick questions and short writing tasks; for more, you need 16 GB.
16 GB: 12B to 14B models
The most common Mac configuration fits the strong mid-size models at Q4: Gemma 4 12B, Qwen3 14B and Phi-4 14B. On a base M4 expect about 9 to 11 tokens per second, which is comfortable to read along with. gpt-oss 20B only loads at a low Q3 quantization on 16 GB, so the 12B to 14B models are the better pick here.
24 GB: the first mixture-of-experts models
24 GB lets you run Qwen3 30B-A3B and Gemma 4 26B-A4B, though only at Q3. These mixture-of-experts models use just 3 to 4 billion parameters per word, so they are fast for their size: about 19 tokens per second on an M4 and 44 on an M4 Pro. Dense 22B to 27B models also load, but at 6 to 15 tokens per second they feel slower.
32 to 36 GB: 30B models at good quality
With 32 GB on a base M4, Qwen3 30B-A3B fits at Q4 at around 16 tokens per second. The M4 Max with 36 GB is where it gets fast: Qwen3.6 35B-A3B runs at Q4 at roughly 74 tokens per second, and dense 32B models like Qwen3 32B at around 18 tokens per second.
48 GB: the sweet spot
An M4 Pro with 48 GB, for example in a Mac mini, gives a model about 34.6 GB. That fits Qwen3.6 35B-A3B and Qwen3 30B-A3B at Q6, close to full quality, at around 26 tokens per second, and dense 32B models like Gemma 4 31B at Q6. For most people who want strong local models without a Mac Studio, this is the configuration to aim for.
64 GB: 70B models
64 GB is the first size that loads 70B models such as Llama 3.3 70B, at Q3. On an M4 Max that runs at about 10 tokens per second. Many people get more out of 64 GB by running a 30B to 35B mixture-of-experts model at full Q8 quality instead, which is several times faster.
96 to 256 GB: the biggest open models
An M4 Max with 128 GB runs gpt-oss 120B at Q5 at around 28 tokens per second, and Llama 3.3 70B at full Q8. A Mac Studio with an M3 Ultra and 256 GB can load Qwen3 235B-A22B at Q4, one of the largest open models, at roughly 15 tokens per second. Very few single desktop machines can load models this large.
Which Mac to buy for local AI
- Memory first. You cannot add memory later, and it decides which models load at all. Buy the most you can afford.
- Then the chip. Pro is about twice as fast as the base chip, Max about twice again. A Max with 36 GB beats a base chip with 32 GB for speed, but cannot load anything bigger.
- Good targets: 24 GB for 14B models with headroom, 48 GB (M4 Pro) as the value sweet spot, 64 GB or more for 70B models.
Comparing a Mac with a PC? See Mac vs Nvidia for local AI.
Getting started on a Mac
- Install LM Studio or Ollama. LM Studio can use Apple's MLX format, which is often a little faster on Macs.
- Pick a model from the tier above that matches your memory, and start with Q4.
- Close heavy apps before loading a large model. Browsers and design tools share the same memory.
Advanced users can raise the GPU memory limit with the iogpu.wired_limit_mb setting in Terminal to fit slightly larger models. It resets on restart, but leave enough memory for macOS or the system will slow down.
To see every model for your exact Mac, choose Mac in the checker and pick your chip and memory.