The README minimums are usually wrong. This table shows real production VRAM — what your jobs actually need to finish, not the toy example that fit on a developer’s laptop. Sourced from official docs, practitioner benchmarks, and our own verified runs.
Pick a workflow, get the AWS spot cost and recommended hardware from our verified benchmarks.
Live spot vs on-demand prices for L4, A10G, L40S, A100, H100 across us-east-1.
12 workloads, real dollar figures on public reference datasets, cost-per-sample stamped.
Most biotech tool READMEs document the VRAM their toy example needs — the small monomer fold, the single-class 2D classification, the 1×1 docking pose. Production workloads are not toy examples. AlphaFold-Multimer’s official README says it “works on 16 GB” — in practice a 5-chain antibody complex routinely OOMs on a 16 GB T4 and needs at least an A10G (24 GB) to finish.
The numbers above reflect what works for actual production inputs: 3–5 chain complexes, 100K+ particles, 50K+ atom MD systems, real spectral libraries. Where we have first-hand data from a verified Clusterra run, that’s the source. Where we don’t, we cite the most reliable public reference we could find.
Two tools with the same “recommended 16 GB” entry can have wildly different scaling. AlphaFold-Multimer scales quadratically with sequence length because of triangle attention — the difference between a 1,000-residue complex and a 2,000-residue complex is 4× the memory, not 2×. ESMFold scales linearly, so the same jump roughly doubles memory. The “Scales with” column is the part to read carefully.
We recommend specific GPU types — T4, L4, A10G, L40S, A100, H100 — rather than just a VRAM number because compute capability matters too. AlphaFold runs ~3× faster on an L4 than on a T4 with the same 16 GB. Boltz-2 needs Ampere or newer (sm_75 is not enough). The recommendations bake in compute generation, not just memory.
All GPUs listed are available on AWS in us-east-1. The
spot price comparator shows live pricing,
typically 50–80% off on-demand.
Once you know VRAM, the next question is “which AWS instance, and what does it cost on spot?” For that, see our spot price comparator or workflow cost calculator. Or just look at the verified benchmarks — each row shows the actual instance, runtime, and dollar figure on a real public dataset.