Running Local LLMs on Apple Silicon in 2026
Unified memory makes Macs surprisingly good at big models, and disappointingly slow at fast generation. Where the M-series shines and where it does not.
The Apple Silicon story for local LLMs is two sentences. Unified memory means the GPU can use up to ~75% of system RAM. Memory bandwidth is much lower than NVIDIA datacenter cards. Both matter.
The numbers
| Mac | Unified RAM | Bandwidth | tok/s on Llama 3.1 8B Q4 | |-----|-------------|-----------|--------------------------| | M4 Pro 48 GB | 36 GB usable | 273 GB/s | ~70 | | M3 Max 128 GB | 96 GB usable | 400 GB/s | ~110 | | M4 Max 128 GB | 96 GB usable | 546 GB/s | ~140 | | M2 Ultra 192 GB | 144 GB usable | 800 GB/s | ~200 | | M4 Ultra 256 GB | 192 GB usable | 1092 GB/s | ~270 |
Where Macs win
- Quiet single-machine inference. No fan noise, no second PSU.
- Fitting big models. M4 Ultra 256 GB runs DeepSeek V3 at Q4. No NVIDIA consumer card touches that.
- Power. The whole machine pulls 30-100W during inference. A 4090 alone pulls 400+.
- Battery laptops. M4 Max in a MacBook Pro lets you run a local LLM on a plane.
Where Macs lose
- Tokens per second on small models. A 4090 demolishes any Mac on 7B Q4 simply through bandwidth.
- Multi-user serving. macOS is not a serving stack. Linux on NVIDIA wins for any team scenario.
- Software polish. vLLM, TensorRT-LLM, ExLlamaV2 all target NVIDIA first. llama.cpp Metal is excellent but not the fastest path on every model.
- Training. The MLX framework is improving but still well behind PyTorch on CUDA.
Buying advice
If your goal is to run 70B-class models on a single quiet machine and you do not need maximum tok/s, buy a Mac. M4 Max 128 GB is the sweet spot for most people. M4 Ultra 256 GB is the flex if you want DeepSeek V3 to fit.
If your goal is fast 7B-13B for one user, buy a 4090. If you want 70B with throughput, buy 2x 3090 or rent cloud.
The calculator on the home page includes every Mac listed above and computes the actual tok/s you should expect.