text model · Qwen2.5-Coder · macOS
Can I run Qwen2.5 Coder 0.5B on Apple M2 (16GB)?
Yes. Qwen2.5 Coder 0.5B runs on Apple M2 (16GB) at Q4_K_M (~1.4 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~1.4 GB of ~10.5 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.4 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 qwen2.5-coder:0.5b llama-cli -hf bartowski/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M lms get bartowski/Qwen2.5-Coder-0.5B-Instruct-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
- 0.494B
- Q4_K_M size
- 0.37 GB
- Q8_0 size
- 0.49 GB
- Context
- 32k
- Ollama tag
- qwen2.5-coder:0.5b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run Qwen2.5 Coder 0.5B on other hardware
FAQ
Can Apple M2 (16GB) run Qwen2.5 Coder 0.5B?
Yes. Qwen2.5 Coder 0.5B runs on Apple M2 (16GB) at Q4_K_M (~1.4 GB of ~10.5 GB usable).
How much memory does Qwen2.5 Coder 0.5B need?
Apple M2 (16GB) has room to spare. At Q4_K_M the weights are ~0.37 GB; with KV cache and runtime overhead, budget ~1.4 GB at a 4k context.
What is the best tool to run Qwen2.5 Coder 0.5B 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.