Gemma 4 - Multimodal AI

By meet-aiView on GitHub →

ComfyUI custom nodes for Gemma 4 multimodal AI

How much VRAM does Gemma 4 - Multimodal AI require?

Direct Answer: The ComfyUI node Gemma 4 - Multimodal AI requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.

High (2-4GB)
Base VRAM:
128MB (0.1GB)
Recommended GPU:
4GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
MEDIUM

Interactive VRAM Compatibility Estimator

Estimated Total VRAM: 3.00 GBTarget: 8 GB
✅ Comfortable Fit

Your GPU has plenty of headroom. You can run this node safely with your active configurations!

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What Python packages are required for Gemma 4 - Multimodal AI?

Direct Answer: Running Gemma 4 - Multimodal AI requires installing the following Python package dependencies: modelscope, numpy, pillow, torch. Ensure your ComfyUI environment has these packages active before launching.

requirements.txt
modelscope
numpy
pillow
torch

Interactive Setup & Dependency Resolver

Operating System:
Environment Type:
Run this terminal command in your ComfyUI root folder:
# Loading command...

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Frequently Asked Questions

How much VRAM does Gemma 4 - Multimodal AI require?

Gemma 4 - Multimodal AI requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.

Can I run Gemma 4 - Multimodal AI on an RTX 3060, RTX 4070, or RTX 4090?

✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom

What PyTorch version does Gemma 4 - Multimodal AI need?

Gemma 4 - Multimodal AI requires the following PyTorch-related packages: torch. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).

What Python packages are required for Gemma 4 - Multimodal AI?

To run Gemma 4 - Multimodal AI, you need to install: modelscope, numpy, pillow, torch. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running Gemma 4 - Multimodal AI?

Gemma 4 - Multimodal AI supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.

How do I install Gemma 4 - Multimodal AI in ComfyUI?

To install Gemma 4 - Multimodal AI: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/mailzwj/ComfyUI-Gemma4, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.