Can NVIDIA GeForce RTX 4080 run scGPT?
50M parameter Scientific Computing model on 16GB GDDR6X
VRAM Requirements
scGPT is a 50M parameter model. At full precision (FP16), it requires 12GB of VRAM. Your NVIDIA GeForce RTX 4080 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 4080 runs scGPT comfortably for large single-cell datasets (100K+ cells). Fine-tune for cell type annotation, perturbation prediction, and multi-batch integration. With 16GB VRAM, you can handle atlas-scale datasets that would be impractical on smaller GPUs.
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 scGPT
pip install scgptFoundation model for single-cell RNA-seq. Requires scanpy and anndata for data handling.
Step 3: Load pre-trained model
Download pre-trained checkpoints from the scGPT GitHub repository. Fine-tune on your dataset for cell type annotation, perturbation prediction, or batch integration.
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 4080 Specs
Other GPUs That Run scGPT
Other Scientific Computing Models on NVIDIA GeForce RTX 4080
About scGPT
Foundation model for single-cell RNA-seq analysis. Fine-tune for cell type annotation, gene perturbation prediction, multi-batch integration, and multi-omics analysis. VRAM scales with dataset size — 100K+ cells need 12GB, smaller datasets fit on 4-8GB. Replaces traditional pipelines like Scanpy for many tasks.