text model · Llama 3.2 Vision · Windows
Can I run Llama 3.2 Vision 11B on 8GB RAM Laptop (CPU/iGPU only)?
No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~9 GB even at Q4_K_M, but only ~5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 4 GB: Llama 3.2 Vision 11B needs roughly 9 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Llama 3.2 Vision 11B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Llama 3.2 Vision 11B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- 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
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Llama 3.2 Vision 11B?
No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Llama 3.2 Vision 11B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-3.2-vision-11b/laptop-8gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.