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known_issues.md

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Known Issues

Numerical performance for different GPU devices

There are numerical performance issues with some GPU types that are under investigation, see this Issue for tracking.

Verified devices

We have run successful large-scale numerical tests for the following devices and maximum number of tokens:

  • H100 80 GB: up to 5,120 tokens.
  • A100 80 GB: up to 5,120 tokens.
  • A100 40 GB: up to 4,352 tokens with unified memory configuration.
  • P100 16 GB: up to 1,024 tokens.

Note that the 80 GB devices can run larger targets using unified memory, but outputs have only been verified on particular examples rather than a large-scale test set.

CUDA Capability 7.x GPUs: known issues

All CUDA Capability 7.x GPUs (e.g. V100) produce obviously bad output, with lots of clashing residues (the clashes cause a ranking score of -99 or lower). With a small fix relating to bfloat16 conversion to float32 outputs look normal, but there are numerical performance regressions for some bucket sizes (tested on V100 devices).

CUDA Capability 6.x GPUs: no known issues

CUDA Capability 6.x GPUs give reasonable output, but large scale numerical testing has only been done for P100.

Incorrect handling of two-letter atoms in SMILES ligands

Between commits https://github.com/google-deepmind/alphafold3/commit/f8df1c7 and https://github.com/google-deepmind/alphafold3/commit/4e4023c, AlphaFold 3 handled incorrectly any two-letter atoms (e.g. Cl, Br) in ligands defined using SMILES strings.