Best GPU for AlphaFold & Protein Structure Prediction

AlphaFold 2's VRAM usage is dominated by the Evoformer's attention mechanism, which scales quadratically with sequence length and MSA depth. The model weights themselves are tiny (~200MB). This means VRAM requirements depend entirely on what you're predicting:

ColabFold tip: ColabFold uses MMseqs2 instead of the full BFD/Uniclust databases. This eliminates the 2.5TB database download and reduces CPU RAM needs from 128GB to 16-32GB. Quality is nearly identical for most proteins. If you're setting up locally for the first time, start with ColabFold.

Batch processing: If you're predicting structures for hundreds of proteins (e.g., from a proteomics experiment), VRAM determines your maximum protein size but not throughput. The GPU processes one protein at a time. Throughput comes from having a fast CPU for MSA search and fast NVMe storage for the databases.

ESM-2 is Meta's family of protein language models. The key difference from AlphaFold: ESMFold predicts structure from a single sequence — no MSA needed. This makes it dramatically faster (seconds vs minutes) at the cost of slightly lower accuracy.

*ESMFold at FP16 uses ~16GB for most proteins due to optimized inference, even though the full ESM-2 15B model is 30GB.

When to use ESMFold vs AlphaFold: Use ESMFold when you need speed — screening hundreds of sequences, exploring mutant libraries, or getting quick structural hypotheses. Use AlphaFold when accuracy matters most — publication figures, drug target analysis, or complex multimer modeling. Many labs use ESMFold as a first pass and AlphaFold for the hits.

ESM-2 embeddings: Beyond structure prediction, ESM-2 embeddings are the new standard for protein representation. Use them for function prediction, variant effect scoring (think EVE/ESM-1v), binding site identification, and protein family classification. The 3B model runs on any 8GB+ GPU. The 15B model is better but needs 32GB+ for FP16.

scGPT brings the foundation model paradigm to single-cell biology. Instead of building analysis pipelines from scratch (Scanpy + scVI + CellTypist + ...), you fine-tune a single pre-trained model for your specific task: cell type annotation, perturbation prediction, gene network inference, or batch integration.

VRAM scaling: The model itself is only 50M parameters (~200MB). VRAM is dominated by the gene expression matrix during training and fine-tuning:

Practical tip: If your dataset exceeds your VRAM, use gradient checkpointing (trades speed for memory) or subsample during training. For inference only (applying a fine-tuned model to new data), VRAM requirements are much lower — the model processes cells in batches.

RFdiffusion from the Baker Lab generates novel protein structures through a diffusion process. Use cases include de novo binder design, motif scaffolding, and symmetric assembly generation. VRAM requirements depend on protein size:

• +Small designs (<200 residues): 8GB
• +Medium designs (200-500 residues): 10-12GB
• +Complex multi-chain assemblies: 16GB+

Unlike AlphaFold, RFdiffusion is generative — you typically run hundreds of design trajectories and filter the results. Throughput matters. A faster GPU (RTX 4090 vs 3090) generates more designs per hour, even though both have enough VRAM.