ESM-IF1 inverse-folding remote tool#
TOU validation and deployment status (2026-08-16)#
ESM-IF1 sequence design ran on the NVIDIA GB10 host (aarch64, 20 cores, cc 12.1, driver 580.173.02) on the CPU, under the optimization kit’s
fastmode (leverbatched_sampling). This is the first provider on the standard with[gpu] policy = "none", and that is a measurement rather than a fallback. The kit pins torch 2.4.0, whose CUDA generation predates this card: cuBLAS 12.4 for aarch64 carries cubins only through sm_90. It does carry sm_90 PTX, so a forward JIT is not ruled out and the honest status of that route is unmeasured rather than impossible —provider.toml’s[gpu]section has thecuobjdumpoutput and the three reasons the route was not taken anyway, the first being that the kit’s pinned PyG companions have no aarch64 build at any version. Four consecutive identical requests produced byte-identical FASTA files, and the same sha256 as the kit’s own CLI. Peak RSS is flat in length and driven by the product of length and sample rows: 2.45 GiB from 64 through 395 residues, 3.65 at 800x2, and 11.22 GiB at the worst request the declared ceilings admit together (1600 tokens x 4 rows, 757.7 s). Schedule on 11.22 unless the deployment caps the input; the smoke’s 2.45 would under-budget an ordinary request by 4.6x. Public publication, cross-user isolation, concurrency, recovery and design quality are untested; keep this deployment private.
Operation:
esm_if1_designStart:
python -m tooluniverse.remote.esm_if1.esm_if1_toolEndpoint:
http://127.0.0.1:8050/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. Read the measured budget and the envelope it was measured at in the manifest before co-tenanting anything else on the card.
TOU check:
tu doctor --forward http://127.0.0.1:8050/mcp --jsonPrivate relay:
tu serve --share --forward http://127.0.0.1:8050/mcp --name validation-esm-if1 --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 esm_if1. 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#
ESM-IF1 inverse folding: sampled sequences for a protein backbone (fair-esm GVP-Transformer, 142M)
Weights and kit are pinned: src/tooluniverse/remote/esm_if1/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/esm_if1.json, and that per-host file – not the
number in the manifest – is what a scheduler should read.