text model · Qwen3 · macOS
Can I run Qwen3 8B on Apple M2 (16GB)?
Yes. Qwen3 8B runs on Apple M2 (16GB) at Q4_K_M (~6.5 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~6.5 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~6.5 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run qwen3:8b llama-cli -hf Qwen/Qwen3-8B-GGUF:Q4_K_M lms get Qwen/Qwen3-8B-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
- 8B
- Q4_K_M size
- 5.03 GB
- Q8_0 size
- 8.71 GB
- Context
- 32k
- Ollama tag
- qwen3:8b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run Qwen3 8B on other hardware
FAQ
Can Apple M2 (16GB) run Qwen3 8B?
Yes. Qwen3 8B runs on Apple M2 (16GB) at Q4_K_M (~6.5 GB of ~10.5 GB usable).
How much memory does Qwen3 8B need?
Apple M2 (16GB) has room to spare. At Q4_K_M the weights are ~5.03 GB; with KV cache and runtime overhead, budget ~6.5 GB at a 4k context.
What is the best tool to run Qwen3 8B 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.