Can NVIDIA GeForce RTX 4060 Ti 16GB run ESM-2 3B?
3B parameter Scientific Computing model on 16GB GDDR6
VRAM Requirements
ESM-2 3B is a 3B parameter model. At full precision (FP16), it requires 6GB of VRAM. Your NVIDIA GeForce RTX 4060 Ti 16GB has 16GB — enough to run it without any quantization.
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 4060 Ti 16GB's 16GB VRAM runs ESM-2 3B 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 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 ~6GB used.
NVIDIA GeForce RTX 4060 Ti 16GB Specs
Other GPUs That Run ESM-2 3B
Other Scientific Computing Models on NVIDIA GeForce RTX 4060 Ti 16GB
About ESM-2 3B
Meta's protein language model — the workhorse for protein embeddings, function prediction, and variant effect analysis. The 3B parameter version runs on virtually any modern GPU. Use the embeddings for downstream tasks like binding site prediction, fitness landscape mapping, and protein classification.