text model · Qwen3 · macOS
Can I run Qwen3 30B-A3B on Apple M5 (16GB)?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
Needs ~20.7 GB even at Q4_K_M, but only ~10.5 GB is usable.
- Q4_K_M needed
- ~20.7 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- 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
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Qwen3 30B-A3B on other hardware
FAQ
Can Apple M5 (16GB) run Qwen3 30B-A3B?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
How much memory does Qwen3 30B-A3B need?
Apple M5 (16GB) does not have enough memory. 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.
Sources
Memory figures are estimates. See methodology.