text model · Llama 3.2 Vision · Windows
Can I run Llama 3.2 Vision 11B on Nvidia GeForce RTX 4090 (24GB)?
Yes. Llama 3.2 Vision 11B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~9 GB of ~23 GB usable).
Runs at Q4_K_M using ~9 GB of ~23 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~23 GB
- Device memory
- 24 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
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can Nvidia GeForce RTX 4090 (24GB) run Llama 3.2 Vision 11B?
Yes. Llama 3.2 Vision 11B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~9 GB of ~23 GB usable).
How much memory does Llama 3.2 Vision 11B need?
Nvidia GeForce RTX 4090 (24GB) 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.