Can NVIDIA Tesla P40 run AlphaFold 2?
93M parameter Scientific Computing model on 24GB GDDR5X
VRAM Requirements
AlphaFold 2 is a 93M parameter model. At full precision (FP16), it requires 16GB of VRAM. Your NVIDIA Tesla P40 has 24GB — enough to run it without any quantization.
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
NVIDIA Tesla P40's 24GB VRAM comfortably runs AlphaFold 2 for most proteins, including long sequences (1000+ residues) and multimer predictions. You can process multiple sequence alignments locally without worrying about VRAM limits. This is a professional-grade setup for structural biology research.
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 ~16GB used.
NVIDIA Tesla P40 Specs
Other GPUs That Run AlphaFold 2
Other Scientific Computing Models on NVIDIA Tesla P40
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.