Can NVIDIA GeForce RTX 4070 SUPER run RFdiffusion?
200M parameter Scientific Computing model on 12GB GDDR6X
VRAM Requirements
RFdiffusion is a 200M parameter model. At full precision (FP16), it requires 16GB of VRAM. Your NVIDIA GeForce RTX 4070 SUPER has 12GB, so you'll need to quantize it to 8-bit (Q8) to fit.
Maximum quality, no quantization
Near-lossless, ~50% size reduction
Good quality, ~75% size reduction
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)
What This Means in Practice
RFdiffusion at mixed precision on NVIDIA GeForce RTX 4070 SUPER delivers strong performance for most workloads. A solid setup for routine scientific computing tasks.
How to Set It Up
Step 1: Set up Python environment
conda create -n scicomp python=3.10 && conda activate scicompA clean Conda environment avoids dependency conflicts. Python 3.10 is recommended for most scientific computing tools.
Step 2: Install RFdiffusion
git clone https://github.com/RosettaCommons/RFdiffusion.git && cd RFdiffusion && pip install -e .Protein design through diffusion from the Baker Lab. Requires PyTorch with CUDA support.
Step 3: Run protein design
See the RFdiffusion GitHub for examples: unconditional generation, binder design, motif scaffolding, and symmetric assemblies.
Step 4: Verify GPU is being used
nvidia-smiCheck that VRAM usage increases when the model loads. You should see ~10GB used.
NVIDIA GeForce RTX 4070 SUPER Specs
Other GPUs That Run RFdiffusion
Other Scientific Computing Models on NVIDIA GeForce RTX 4070 SUPER
About RFdiffusion
Protein design through diffusion — generate novel protein structures, design binders for therapeutic targets, and scaffold functional motifs. From the Baker Lab at UW. VRAM usage depends on protein size; most designs fit on 8-10GB but complex multi-chain assemblies need 16GB+.