text model · Qwen3 · macOS
Can I run Qwen3 0.6B on Apple M4 (24GB)?
Yes. Qwen3 0.6B runs on Apple M4 (24GB) at Q4_K_M (~1.5 GB of ~16 GB usable).
Runs at Q4_K_M using ~1.5 GB of ~16 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.5 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run qwen3:0.6b llama-cli -hf bartowski/Qwen_Qwen3-0.6B-GGUF:Q4_K_M lms get bartowski/Qwen_Qwen3-0.6B-GGUF 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.
- Parameters
- 0.6B
- Q4_K_M size
- 0.48 GB
- Q8_0 size
- 0.8 GB
- Context
- 32k
- Ollama tag
- qwen3:0.6b
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run Qwen3 0.6B on other hardware
FAQ
Can Apple M4 (24GB) run Qwen3 0.6B?
Yes. Qwen3 0.6B runs on Apple M4 (24GB) at Q4_K_M (~1.5 GB of ~16 GB usable).
How much memory does Qwen3 0.6B need?
Apple M4 (24GB) has room to spare. At Q4_K_M the weights are ~0.48 GB; with KV cache and runtime overhead, budget ~1.5 GB at a 4k context.
What is the best tool to run Qwen3 0.6B 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.