> This page is for Boltz-2, version 1.10.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.10.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/boltz2/1.10.0/llms.txt
> - 1.9.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.9.0/llms.txt
> - 1.8.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.8.0/llms.txt
> - 1.7.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.7.0/llms.txt
> - 1.6.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.6.0/llms.txt
> - 1.5.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.5.0/llms.txt
> - 1.4.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.4.0/llms.txt
> - 1.3.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.3.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.0.0/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

## Supported Hardware

### Minimum System Hardware Requirements

| CPU Cores    | RAM (GB) | NVMe SSD Storage (GB) | GPU Count |
|--------|-----------------|-----------|-------|
| 12  | 64              | 80       | One or more NVIDIA GPUs from the list of supported NVIDIA GPUs |

<Note>
The NVMe SSD storage requirement of 80 GB accounts for the Boltz-2 NIM image size (about 73 GB of disk usage). It also includes additional space for Docker layer caching and temporary files during runtime.
</Note>

### Supported NVIDIA GPUs

The Boltz-2 NIM requires NVIDIA GPUs with at least 48 GB of GPU Memory. While the system can run on any NVIDIA GPU meeting this minimum memory requirement, the following configurations have been specifically tested and optimized with the optimized backend:

| GPU    | GPU Memory (GB) | Precision | GPU Count |
|--------|-----------------|-----------|-----------|
| NVIDIA A100-SXM4-80GB   | 80              | BF16      |  1-2-4-8  |
| NVIDIA B200   | 180             | BF16      |  1-2-4-8  |
| NVIDIA B300 (NVIDIA-B300-SXM6-AC)   | 288             | BF16      |  1-2-4-8  |
| NVIDIA GB10 (DGX Spark)   | 128             | BF16      |  1  |
| NVIDIA GB200   | 186             | BF16      |  1-2-4-8  |
| NVIDIA GB300 (Grace Blackwell Ultra)   | 288             | BF16      |  1-2-4-8  |
| NVIDIA GH200 144GB   | 144             | BF16      |  1-2-4-8  |
| NVIDIA H100 80GB HBM3   | 80              | BF16      |  1-2-4-8  |
| NVIDIA H200   | 141             | BF16      |  1-2-4-8  |
| NVIDIA L40S   | 48              | BF16      |  1-2-4-8  |
| NVIDIA RTX 6000 Ada Generation   | 48              | BF16      |  1-2-4-8  |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition   | 96              | BF16      |  1-2-4-8  |

<Note>
The GB10 uses 128 GB of LPDDR5x unified memory, shared between the CPU and the GPU on the NVIDIA Grace Blackwell Superchip.
</Note>

For performance and stability, use one of the tested configurations above. Other NVIDIA GPUs with 48 GB or more memory might work, but they have not been tested and might show lower performance or stability.

### Model Profile

Release 1.9.0 ships a single untagged model profile. Auto-selection uses this profile on every supported GPU:

| Release | Profile ID | Tags |
|---------|------------|------|
| 1.9.0 | `af1349b9f5f9a1a6a0404dea36dcc9499bcb25c9adc112b7cc9a93cae41f3262` | None. Used on all supported GPUs. |

Leave `NIM_MODEL_PROFILE` unset unless you need to pin this profile for reproducibility. Per-GPU profile hashes from 1.8.0 and earlier are not in the 1.9.0 manifest and must not be used. Refer to [Select a profile manually](/nim/bionemo/boltz2/optimization#select-a-profile-manually).


## Test Locally Available Hardware

* Begin with [Docker-supported operating system](https://docs.docker.com/engine/install/#installation-procedures-for-supported-platforms)
* Install [Docker](https://docs.docker.com/engine/install/) - minimum version: 23.0.1
* Install [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/) - minimum version: 590.44.01 with CUDA 13.1
* Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-the-nvidia-container-toolkit) - minimum version: 1.13.5
* Verify your container runtime supports NVIDIA GPUs by running

```bash
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```

Example output:

```bash
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 590.44.01   Driver Version: 590.44.01    CUDA Version: 13.1    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA H100 80GB HBM3  Off  | 00000000:01:00.0 Off |                    0 |
| N/A   35C    P0    45W / 700W |      0MiB / 81559MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
```

<Note>
For more information on enumerating multi-GPU systems, refer to the NVIDIA Container Toolkit [GPU enumeration](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html#gpu-enumeration) topic.
</Note>