text model · Qwen3 · macOS
Can I run Qwen3 30B-A3B on Apple M4 Max (64GB)?
Yes. Qwen3 30B-A3B runs on Apple M4 Max (64GB) at Q4_K_M (~20.7 GB of ~48 GB usable).
Runs at Q4_K_M using ~20.7 GB of ~48 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Max (64GB) leaves ~27.3 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~20.7 GB
- Usable on device
- ~48 GB
- Device memory
- 64 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run qwen3:30b-a3b llama-cli -hf unsloth/Qwen3-30B-A3B-GGUF:Q4_K_M lms get unsloth/Qwen3-30B-A3B-GGUF - Parameters
- 30.5B (MoE, 3.3B active)
- Q4_K_M size
- 18.6 GB
- Q8_0 size
- 32.5 GB
- Context
- 32k
- Ollama tag
- qwen3:30b-a3b
- Memory
- 64 GB unified
- Usable for weights
- ~48 GB
- Power draw
- ~145 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Qwen3 30B-A3B on other hardware
FAQ
Can Apple M4 Max (64GB) run Qwen3 30B-A3B?
Yes. Qwen3 30B-A3B runs on Apple M4 Max (64GB) at Q4_K_M (~20.7 GB of ~48 GB usable).
How much memory does Qwen3 30B-A3B need?
Apple M4 Max (64GB) has room to spare. At Q4_K_M the weights are ~18.6 GB; with KV cache and runtime overhead, budget ~20.7 GB at a 4k context. It is a Mixture-of-Experts model (30.5B total / 3.3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3 30B-A3B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3-30b-a3b/apple-m4-max-64gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.