OpenFold3 (OpenBind-0) remote tool#
TOU validation and deployment status (2026-08-16)#
OpenFold3 0.5.0 with the OpenBind-0 parameters, under the optimization kit’s
fastmode, ran on the NVIDIA GB10 (aarch64, cc 12.1, driver 580.173.02) withpair_transitionablated through the kit’s own MODEL_OPT_LEVERS_OFF door, the kit’spost_releasegate forced on every item, and the data loader forced to 0 workers. All fivetu remote setupsteps green. Verified on the whole acceptance sequence, not a single smoke: an ascending 100/200/400-residue ladder (3/3), a mixed-shape sequence 100/100/200/100 (4/4), FOUR CONSECUTIVE IDENTICAL REQUESTS (4/4), and three consecutive 800-residue requests (3/3) – five mmCIF models every time. TWO independent second-request defects had to be fixed first, one in the kit’s shared core and one in upstream 0.5.0 itself, and only one of them was findable by repeating a shape. Peak reserved 2.20 GiB at 35 residues rising to 9.97 GiB at 800, which is the declared budget and the ceiling the tool enforces. Wall times were taken with one to three other providers’ workers on the card and are not measurements; residency returns 1.25x at 35 residues and 1.13x at the 800-residue budget row against a fresh process per request, because ~30 s of each request is Lightning and featurisation rather than the model. Public publication, cross-user isolation, concurrency, recovery and biological accuracy are untested; keep this deployment private.
Operation:
openfold3_ob0_foldStart:
python -m tooluniverse.remote.openfold3_ob0.openfold3_ob0_toolEndpoint:
http://127.0.0.1:8052/mcpProvider configuration: this provider is on the remote-tool standard, so the model runs in its own environment built from a pinned lock, and the weights are declared with a size and a sha256 and verified before the worker starts. Keep provider data, weights, caches and credentials outside Git. Use one worker: the resident worker serves one request at a time by design, and a second in-flight request queues on its lock rather than doubling peak VRAM. The measured VRAM budget is 9.97 GiB; read the envelope it was measured at in the manifest before co-tenanting anything else on the card, because a larger request exceeds it.
TOU check:
tu doctor --forward http://127.0.0.1:8052/mcp --jsonPrivate relay:
tu serve --share --forward http://127.0.0.1:8052/mcp --name validation-openfold3-ob0 --workers 1
Non-loopback binding requires TOOLUNIVERSE_API_TOKEN; otherwise keep the
server on loopback. The relay requires TOOLUNIVERSE_SERVICE_KEY. Public
publication and independent-caller testing were not run.
New-user check: python scripts/remote_validation/setup_skill_preflight.py --implementation openfold3_ob0. Add --check-provider-env before launch, --live
after launch, and --check-connect-prereqs before sharing. Live preflight
checks exact MCP discovery only; it does not run or validate a model.
The authenticated 2026-08-16 Platform matrix found all 30 private owner relays
online and all 41 operations discoverable. It imported each implementation as
unpublished owner draft(s), configured a 120-second timeout, and invoked it
through /expert-sessions/{id}/test. This implementation was added after
that matrix and was not part of it, so none of that evidence covers it: it has
not been exercised through the Platform at all, and everything above was
measured locally, on one card, through the loopback endpoint. Public
publication, independent-caller authorization/isolation, broad saturation and
persistent supervision are unvalidated here.
Overview#
OpenFold3 0.5.0 (OpenBind-0) all-atom structure prediction from a query of chains
Weights and kit are pinned: src/tooluniverse/remote/openfold3_ob0/provider.toml
declares the upstream kit by commit, the environment by lock file, and every
weight file with its byte count and digest. Nothing is fetched at request time.
The measured peak VRAM for this host is written to
~/.cache/tooluniverse/measured/openfold3_ob0.json, and that per-host file – not the
number in the manifest – is what a scheduler should read.