text model · Llama 3.2 Vision · macOS
Can I run Llama 3.2 Vision 11B on Apple M4 Pro (48GB)?
Yes. Llama 3.2 Vision 11B runs on Apple M4 Pro (48GB) at Q4_K_M (~9 GB of ~32 GB usable).
Runs at Q4_K_M using ~9 GB of ~32 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama3.2-vision:11b llama-cli -hf leafspark/Llama-3.2-11B-Vision-Instruct-GGUF:Q4_K_M lms get leafspark/Llama-3.2-11B-Vision-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
- 10.7B
- Q4_K_M size
- 7.36 GB
- Q8_0 size
- 11.49 GB
- Context
- 128k
- Ollama tag
- llama3.2-vision:11b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- Ollama (MLX backend) / MLX direct
You could also run
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can Apple M4 Pro (48GB) run Llama 3.2 Vision 11B?
Yes. Llama 3.2 Vision 11B runs on Apple M4 Pro (48GB) at Q4_K_M (~9 GB of ~32 GB usable).
How much memory does Llama 3.2 Vision 11B need?
Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.
What is the best tool to run Llama 3.2 Vision 11B 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.