Apple M6 for local LLMs
Written by Jakub Rusinowski · Last updated
With 32 GB of LPDDR5X at 170 GB/s, the M6 runs 108 catalogued models at Q4_K_M with 8K context. The largest that fits is Qwen 3 32B (~22.8 GB), and the top pick is Laguna XS 2.1 33B-A3B at 15–32 tok/s.
Apple's first 2 nm chip: 12-core CPU, 12-core GPU with a Neural Accelerator in every GPU core, up to 32 GB of unified memory at 170 GB/s (11% more than the M5). Ships in the Mac mini from 22 September 2026. The TDP figure is a placeholder pending a second source; it does not affect throughput or fit.
Models that run on the M6
Q4_K_M, 8K context, 24 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 24 GB | VRAM | Speed |
|---|---|---|---|
| Laguna XS 2.1 33B-A3B Poolside Laguna XS 2.1 | 20.7 GB | 15–32 tok/s | |
| Nemotron-Cascade 2 30B-A3B Nemotron Cascade 2 | 19.9 GB | 14–30 tok/s | |
| Qwen 3 30B-A3B (MoE) Qwen 3 | 20 GB | 20–41 tok/s | |
| Qwen3-Coder 30B-A3B (MoE) Qwen3-Coder | 20 GB | 20–41 tok/s | |
| GLM-4.7-Flash 30B-A3B GLM-4.7 / GLM-Z1 | 18.9 GB | 16–33 tok/s | |
| Nemotron 3 Nano Omni 30B-A3B Nemotron 3 Nano Omni | 18.9 GB | 16–33 tok/s | |
| North Mini Code 1.0 30B-A3B North Mini Code | 18.9 GB | 16–33 tok/s | |
| Nemotron 3.5 Lightning 30B-A3B Nemotron 3.5 | 18.9 GB | 16–33 tok/s | |
| Gemma 4 26B-A4B Gemma 4 | 16.5 GB | 14–29 tok/s | |
| GPT-OSS 20B GPT-OSS | 13.8 GB | 21–44 tok/s | |
| Gemma 4 E2B Gemma 4 | 3.9 GB | 14–29 tok/s | |
| Gemma 3n E2B Gemma 3n | 4.1 GB | 25–52 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-09-30.
- Memory
- 32 GB LPDDR5X
- Memory bandwidth
- 170 GB/s
- Architecture
- ARM, 2nm TSMC
- Series
- Apple Silicon
- Board power
- 25 W
- Release year
- 2026
- Compute backends
- METAL
- Usable for models
- 24 GB (~75% of unified)
Similar GPUs
Frequently asked questions
Can the Apple M6 run local LLMs?
Yes. With 32 GB (24 GB usable by a model) it runs 108 of the catalogued models at Q4_K_M with 8K context; the largest is Qwen 3 32B, needing about 22.8 GB.
How fast is the Apple M6 for AI inference?
It is estimated to run Llama 3.1 8B at 11–22 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 24 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 32 GB?
Among the best that fit: Laguna XS 2.1 33B-A3B, Nemotron-Cascade 2 30B-A3B, Qwen 3 30B-A3B (MoE), Qwen3-Coder 30B-A3B (MoE), GLM-4.7-Flash 30B-A3B.