text model · Qwen2.5-VL · macOS
Can I run Qwen2.5-VL 7B on Apple M4 (16GB)?
Yes. Qwen2.5-VL 7B runs on Apple M4 (16GB) at Q4_K_M (~7.1 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~7.1 GB of ~10.5 GB usable.
- Q4_K_M needed
- ~7.1 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.
llama-cli -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M lms get ggml-org/Qwen2.5-VL-7B-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.
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.). 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
- 8.29B
- Q4_K_M size
- 5.62 GB
- Q8_0 size
- 9.59 GB
- Context
- 32k
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run Qwen2.5-VL 7B on other hardware
FAQ
Can Apple M4 (16GB) run Qwen2.5-VL 7B?
Yes. Qwen2.5-VL 7B runs on Apple M4 (16GB) at Q4_K_M (~7.1 GB of ~10.5 GB usable).
How much memory does Qwen2.5-VL 7B need?
Apple M4 (16GB) has room to spare. At Q4_K_M the weights are ~5.62 GB; with KV cache and runtime overhead, budget ~7.1 GB at a 4k context.
What is the best tool to run Qwen2.5-VL 7B 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.