Can NVIDIA GeForce RTX 5070 run AlphaFold 2?
93M parameter Scientific Computing model on 12GB GDDR7
VRAM Requirements
AlphaFold 2 is a 93M parameter model. At full precision (FP16), it requires 16GB of VRAM. Your NVIDIA GeForce RTX 5070 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
NVIDIA GeForce RTX 5070 handles AlphaFold 2 for medium-length proteins (up to ~800 residues) without issues. For longer sequences or multimer predictions, you may need to reduce the MSA depth or use CPU offloading. Covers the majority of single-chain predictions comfortably.
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 AlphaFold
pip install alphafoldAlphaFold requires CUDA-compatible GPU drivers and ~2.5TB of genetic database files for MSA. Consider ColabFold (pip install colabfold) for a lighter setup that uses MMseqs2 instead of full databases.
Step 3: Run a prediction
colabfold_batch input.fasta output_dir/ColabFold is the fastest way to get started. For full AlphaFold, use the Docker image from deepmind/alphafold on GitHub.
Step 4: Verify GPU is being used
nvidia-smiCheck that VRAM usage increases when the model loads. You should see ~12GB used.
NVIDIA GeForce RTX 5070 Specs
Other GPUs That Run AlphaFold 2
Other Scientific Computing Models on NVIDIA GeForce RTX 5070
About AlphaFold 2
DeepMind's protein structure prediction model. VRAM usage scales with protein sequence length — short proteins (<500 residues) fit on 8GB, medium sequences need 12GB, and multimers or long proteins (>1000 residues) need 16GB+. The model weights are small (~200MB) but attention on MSA and pair representations dominates memory.