Support Matrix | NVIDIA BioNeMo NIM Documentation

Supported Hardware

Minimum System Hardware Requirements

CPU Cores	RAM (GB)	NVMe SSD Storage (GB)	GPU Count
12	64	80	One or More NVIDIA GPU from the list of Supported NVIDIA GPUs

The NVMe SSD Storage requirement of 80GB accounts for the Boltz-2 NIM image size (~73GB disk usage) plus additional space for Docker layer caching and temporary files during runtime.

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 using NVIDIA TensorRT (TRT):

GPU	GPU Memory (GB)	Precision	GPU Count	Profile Hash
NVIDIA A100-SXM4-80GB	80	BF16	1-2-4-8	`54ad034e2adac67fb8d5508711ff1e23fc100a3f73789924bcebbcd273ee65ed`
NVIDIA B200	180	BF16	1-2-4-8	`28926708ac9dc38b320b8ec7f4787a09b30ed4f997d2800de68bbdcbd94b4064`
NVIDIA B300 (NVIDIA-B300-SXM6-AC)	288	BF16	1-2-4-8	`a471c399e4fda3402ce187cd189266996465d8881635d27d823d286434c90147`
NVIDIA GB10 (DGX Spark)	128	BF16	1	`1ddc2cff848b43fdc285af283cf9dd85fe0d5557d22fdd56db8cf80edc1796de`
NVIDIA GB200	186	BF16	1-2-4-8	`391c862b93968333259743142ea2ddab5a9fe0a82e359d5a4099d2aeb61ab145`
NVIDIA GB300 (Grace Blackwell Ultra)	288	BF16	1-2-4-8	`14d31455ba050ff444a78a1751da193e7f49d9eee252cd0d0c8f8d4d7fc03da6`
NVIDIA GH200 144GB	144	BF16	1-2-4-8	`c79b090925ba0756689b82c733f709a505a1d40b5b123ed0113254a3039dac3e`
NVIDIA H100 80GB HBM3	80	BF16	1-2-4-8	`7dc78ccac59e58eef4e401066c8e8f90dcef358d3c7930effce7bbca1c1bd32e`
NVIDIA H200	141	BF16	1-2-4-8	`e8f54bc1286237ecce8b219882622dd6b689ceab8949c7663c5e58003f4d8ad5`
NVIDIA L40S	48	BF16	1-2-4-8	`663c57b98a8226cacb4881c2bb8a72811d629f17c45ccabe16b6673b44451696`
NVIDIA RTX 6000 Ada Generation	48	BF16	1-2-4-8	`8c0b2b13fd9cae5f637c3b7cdf6746aa0e964a41a91408958cc6c9613e425ba3`
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	96	BF16	1-2-4-8	`552db7646ab5f0864d9256b4f084986c616d04e04b1da2d1c91b30bb37b73b36`

The GB10 features 128GB of LPDDR5x unified memory shared between CPU and GPU, providing seamless memory access across the Grace Blackwell Superchip architecture.

For optimal performance and stability, we recommend using one of the above tested configurations. While other NVIDIA GPUs with 48GB or more memory may work, they have not been officially tested and may exhibit degraded performance or stability issues.

Testing Locally Available Hardware

Begin with Docker-supported operating system
Install Docker - minimum version: 23.0.1
Install NVIDIA Drivers - minimum version: 590.44.01 with CUDA 13.1
Install the NVIDIA Container Toolkit - minimum version: 1.13.5
Verify your container runtime supports NVIDIA GPUs by running

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

Example output:

+-----------------------------------------------------------------------------+
| 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      |
|=============================================================================|
+-----------------------------------------------------------------------------+

For more information on enumerating multi-GPU systems, refer to the NVIDIA Container Toolkit’s GPU Enumeration Docs