Can NVIDIA GeForce RTX 4070 run ESMFold (ESM-2 15B)?
15B parameter Scientific Computing model on 12GB GDDR6X
VRAM Requirements
ESMFold (ESM-2 15B) is a 15B parameter model. At full precision (FP16), it requires 30GB of VRAM. Your NVIDIA GeForce RTX 4070 has 12GB, so you'll need to quantize it to 4-bit (Q4) 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
ESMFold fits on NVIDIA GeForce RTX 4070 at reduced precision. Prediction quality is slightly lower than full precision but still very useful for rapid structural screening. For publication-quality structures, consider full precision on a GPU with more VRAM, or use AlphaFold 2.
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 ESM
pip install fair-esmMeta's ESM models for protein language modeling and structure prediction. Includes ESMFold for single-sequence structure prediction.
Step 3: Run ESMFold prediction
python -c "import esm; model = esm.pretrained.esmfold_v1(); # see docs for full example"ESMFold predicts structures from single sequences — no MSA needed. Much faster than AlphaFold for screening large protein sets.
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 Specs
Other GPUs That Run ESMFold (ESM-2 15B)
Other Scientific Computing Models on NVIDIA GeForce RTX 4070
About ESMFold (ESM-2 15B)
Meta's protein structure prediction using a 15B protein language model. Faster than AlphaFold — predicts structure from a single sequence without MSA lookup. Full precision needs 30GB, but FP16 inference fits on 16GB for most proteins. The quality gap vs AlphaFold has narrowed significantly.