Boltz-2

Inputs and outputs

• Your files are mounted read-only under /inputs inside the container.
• Prediction outputs and metadata should be written to /outputs.
• A shared reference volume is mounted at /ref; Boltz-2 caches model weights under /ref/cache (enforced automatically).
• The container entrypoint is boltz predict, so the job arguments are passed directly to that CLI.

Example 1 — basic MSA-backed run

Use the default pattern shown on the New Job form: point Boltz-2 at your inputs directory, enable the hosted MSA server, and turn on potentials.

/inputs
--use_msa_server
--use_potentials
--out_dir=/outputs

• Place your FASTA and any required config files under /inputs when uploading.
• --use_msa_server tells Boltz-2 to fetch MSAs via the configured remote server.
• --use_potentials enables the learned potentials during inference.
• --out_dir=/outputs directs all prediction artifacts to the job output volume.

Example 2 — local configuration file

You can also drive Boltz-2 with an explicit configuration file that lives under /inputs, while still caching weights under /ref/cache.

/inputs/my_config.yaml
--out_dir=/outputs/my_config_run
--use_msa_server
--use_potentials

• /inputs/my_config.yaml is a Boltz configuration file you prepare based on the upstream examples.
• Outputs for this run will be written under /outputs/my_config_run.
• The platform automatically appends --cache=/ref/cache to keep downloaded weights in the shared reference volume.

Notes

• Boltz-2 jobs currently require a GPU; they are scheduled onto the GPU cluster with 1 GPU, 8 vCPU, and 32 GiB RAM as a baseline.
• For full CLI options and configuration schemas, see the Boltz GitHub repository (boltz-2 section).
• To start a job from the UI, go to New Job → Boltz-2 and paste one of the argument blocks above.