text model · Llama 3.2 Vision · Windows
Can I run Llama 3.2 Vision 11B on 16GB RAM Laptop (CPU/iGPU only)?
Yes. Llama 3.2 Vision 11B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~9 GB of ~12 GB usable).
Runs at Q4_K_M using ~9 GB of ~12 GB usable.
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~12 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 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 AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- 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
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run Llama 3.2 Vision 11B?
Yes. Llama 3.2 Vision 11B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~9 GB of ~12 GB usable).
How much memory does Llama 3.2 Vision 11B need?
16GB RAM Laptop (CPU/iGPU only) 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 Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.