Can NVIDIA GeForce RTX 3090 run ESMFold (ESM-2 15B)?
15B parameter Scientific Computing model on 24GB 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 3090 has 24GB, 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: 48GB DDR5 (2x GPU VRAM minimum for model overflow)
What This Means in Practice
ESMFold at FP16 on NVIDIA GeForce RTX 3090 gives fast single-sequence structure predictions for most proteins. No MSA required — just feed in the amino acid sequence and get a structure in seconds. The speed advantage over AlphaFold makes this ideal for screening large protein sets.
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 ~16GB used.
NVIDIA GeForce RTX 3090 Specs
Other GPUs That Run ESMFold (ESM-2 15B)
Other Scientific Computing Models on NVIDIA GeForce RTX 3090
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.