> This page is for RFD4-Proteina, version 1.0.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.0.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/rfd4-proteina/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Support Matrix

Each design request runs on a single GPU.

## GPU

The RFD4-Proteina NIM has been validated on:

| GPU | GPU Memory | # of GPUs |
|-----------|----------|----------|
| NVIDIA H100 80GB HBM3 | 80GB | 1, 4, 8 |
| NVIDIA H100 NVL | 94GB | 1, 4, 8 |
| NVIDIA H100 PCIe | 80GB | 1, 4, 8 |
| NVIDIA H200 | 141GB | 1, 4, 8 |
| NVIDIA H200 NVL | 141GB | 1, 4, 8 |
| NVIDIA A100-SXM4-80GB | 80GB | 1, 4, 8 |
| NVIDIA A100 80GB PCIe | 80GB | 1, 4, 8 |
| NVIDIA A100-SXM4-40GB | 40GB | 1, 4, 8 |
| NVIDIA A100 40GB PCIe | 40GB | 1, 4, 8 |
| NVIDIA GH200 144GB HBM3e | 144GB | 1, 4, 8 |
| NVIDIA B200 | 180GB | 1, 4, 8 |
| NVIDIA B300 | 288GB | 1, 4, 8 |
| NVIDIA GB200 | 186GB | 1, 4, 8 |
| NVIDIA GB300 | 288GB | 1, 4, 8 |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96GB | 1, 4, 8 |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96GB | 1, 4, 8 |
| NVIDIA RTX 6000 Ada Generation | 48GB | 1, 4, 8 |
| NVIDIA L40S | 48GB | 1, 4, 8 |
| NVIDIA GB10 (DGX Spark) | 128GB | 1 |

<Note>
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](/nim/bionemo/rfd4-proteina/configuration-alternatives) for
running with more than one GPU. The GB10 (DGX Spark) carries a single GB10
superchip, so it is listed as 1.
</Note>

This NIM ships with no refolding oracle built in (refer to
[Oracles](/nim/bionemo/rfd4-proteina/oracle)); 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

| CPU Cores | RAM (GB) | NVMe SSD Storage (GB) | GPU Count |
|---|---|---|---|
| 12 | 16 | 60 | 1 |

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](/nim/bionemo/rfd4-proteina/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](#gpu) table
above. Any [Docker-supported Linux
distribution](https://docs.docker.com/engine/install/#installation-procedures-for-supported-platforms)
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

| Task | Requires an input structure | Requires a ligand |
|---|---|---|
| `generation` | No | No |
| `motif_scaffolding` | Yes | No |
| `enzyme_design` | Yes | No |
| `small_molecule_binder` | Yes | Yes |
| `antibody_design` | Yes | No |
| `partial_diffusion` | Yes | No |
| `inverse_folding` | Yes | No |

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](/nim/bionemo/rfd4-proteina/example-requests) for both dialects in use, and
`/v1/tasks` (refer to
[Getting Started](/nim/bionemo/rfd4-proteina/getting-started#discover-supported-tasks))
for what a running deployment currently serves.