Support Matrix
Each design request runs on a single GPU.
GPU
The RFD4-Proteina NIM has been validated on:
Each design request runs on a single GPU; ”# of GPUs” in the preceding table is the common server configurations this NIM has been deployed on, so a worker pool can serve several requests at once across more than one GPU. Refer to Configuration Alternatives for running with more than one GPU. The GB10 (DGX Spark) carries a single GB10 superchip, so it is listed as 1.
This NIM ships with no refolding oracle built in (refer to Oracles); a mounted oracle that calls an independently hosted structure-prediction NIM adds no GPU memory cost of its own here, since the model it calls lives entirely in that other service.
Minimum System Hardware Requirements
The container downloads model checkpoints into the NIM cache directory on first run; provide a NIM cache location with sufficient free space. Refer to Getting Started for how to set the cache location.
Operating System / Architecture
The container image publishes both linux/amd64 and linux/arm64
manifests, and pulls and serves on both: amd64/x86_64 hosts (H100, H200,
A100, L40S, RTX 6000 Ada, RTX PRO 6000 Blackwell) and arm64/aarch64 Grace
hosts (GB200, GB300, GB10/DGX Spark), all listed in the GPU table
above. Any Docker-supported Linux
distribution
on either architecture is expected to work; the amd64 image is built FROM the
nvcr.io/nvidia/pytorch:26.05-py3 base image, and no distro-specific
behavior is introduced on top of it.
Supported Tasks and Input Dialects
Two input dialects are supported for every task that requires a structure:
annotated_cif: a single self-contained annotated mmCIF file.condition_spec: a structured condition specification (YAML/JSON) plus the structure file(s) it references.
Refer to Example Requests for both dialects in use, and
/v1/tasks (refer to
Getting Started)
for what a running deployment currently serves.