docs/gpu_plan.md: cost-efficient plan for running AF3-class co-folding
(Boltz-2/DiffDock) on a GPU then paying nothing when idle.
- Key insight: structure-track data is tiny (MB of PDBs/SMILES); only the
GPU + model weights are heavy -> serverless is ideal.
- Recommend Modal (per-second billing, scales to zero = nothing to kill);
RunPod as the SSH-box alternative with idle auto-terminate.
- Lifecycle: image -> weights Volume (cache, don't re-download) -> run ->
git push small results -> teardown automatic.
- Phase 1 validate on 3 known binders (~$1) before paying for a screen;
Boltz-2 (affinity) on an L4/A10 (24-48GB); est total ~$5-15.
gpu/modal_app.py: Modal app skeleton (image, weights volume, GPU cofold()
function, local entrypoint); boltz invocation stubbed with TODOs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Junior B.
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