text model · Qwen2.5 · macOS
Can I run Qwen2.5 3B on Apple M2 (16GB)?
Yes. Qwen2.5 3B runs on Apple M2 (16GB) at Q4_K_M (~3.3 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~3.3 GB of ~10.5 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~3.3 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 qwen2.5:3b llama-cli -hf Qwen/Qwen2.5-3B-Instruct-GGUF:Q4_K_M lms get Qwen/Qwen2.5-3B-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
- 3.09B
- Q4_K_M size
- 2.1 GB
- Q8_0 size
- 3.62 GB
- Context
- 128k
- Ollama tag
- qwen2.5:3b
- 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 3B on other hardware
FAQ
Can Apple M2 (16GB) run Qwen2.5 3B?
Yes. Qwen2.5 3B runs on Apple M2 (16GB) at Q4_K_M (~3.3 GB of ~10.5 GB usable).
How much memory does Qwen2.5 3B need?
Apple M2 (16GB) has room to spare. At Q4_K_M the weights are ~2.1 GB; with KV cache and runtime overhead, budget ~3.3 GB at a 4k context.
What is the best tool to run Qwen2.5 3B 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.