Can NVIDIA GeForce RTX 4090 run RFdiffusion?
200M parameter Scientific Computing model on 24GB GDDR6X
VRAM Requirements
RFdiffusion is a 200M parameter model. At full precision (FP16), it requires 16GB of VRAM. Your NVIDIA GeForce RTX 4090 has 24GB — enough to run it without any quantization.
Maximum quality, no quantization
Near-lossless, ~50% size reduction
Good quality, ~75% size reduction
Recommended system RAM: 48GB DDR5 (2x GPU VRAM minimum for model overflow)
What This Means in Practice
NVIDIA GeForce RTX 4090's 24GB VRAM runs RFdiffusion at full precision with room for large inputs. This is a professional-grade setup for computational biology workflows.
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 ~16GB used.
NVIDIA GeForce RTX 4090 Specs
Other GPUs That Run RFdiffusion
Other Scientific Computing Models on NVIDIA GeForce RTX 4090
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+.