Support Matrix#

Software#

  • CUDA 12.9 is configured in the microservice container.

  • NVIDIA GPU Driver version 525 and higher. NVIDIA verified the documentation using 570.133.20.

  • A container runtime environment such as Docker or Kubernetes. For Docker, refer to the instructions from Docker.

  • NVIDIA Container Toolkit installed and configured. Refer to installation in the toolkit documentation.

  • An active subscription to an NVIDIA AI Enterprise product or be an NVIDIA Developer Program member. Access to the containers and models is restricted.

  • An NGC API key. You need the key to download the container from NVIDIA NGC. The container uses the key to download models from NVIDIA NGC. Refer to Generating Your NGC API Key in the NVIDIA NGC User Guide for more information.

About Model Profiles#

The models for NVIDIA NIM microservices use model engines that are tuned for specific NVIDIA GPU models, number of GPUs, precision, and so on. NVIDIA produces model engines for several popular combinations and these are referred to as model profiles. Each model profile is identified by a unique 64-character string of hexadecimal digits that is referred to as a profile ID.

The NIM microservices support automatic profile selection by determining the GPU model and count on the node and attempting to match the optimal model profile. Alternatively, NIM microservices support running a specified model profile, but this requires that you review the profiles and know the profile ID.

The available model profiles are stored in a file in the NIM container file system. The file is referred to as the model manifest file and the default path is /opt/nim/etc/default/model_manifest.yaml in the container.

NVIDIA Llama 3.1 Nemotron Safety Guard Multilingual 8B V1 NIM Model Profiles#

The model requires 48 GB of GPU memory. NVIDIA developed and tested the microservice using the following GPUs:

  • B200

  • H200

  • H100

  • H100 NVL

  • A100 PCIe 40GB and 80GB (supported with generic model profiles only)

  • A100 SXM4 40GB and 80GB

  • A10G

  • L40S

  • RTX A6000 Ada

You can use a single GPU with that capacity or two GPUs that meet the capacity.

For information about locally-buildable and generic model profiles, refer to Model Profiles in NVIDIA NIM for LLMs in the NIM for LLMs documentation.

Locally-Buildable Model Profiles#

Precision

# of GPUs

LoRA

LLM Engine

TensorRT-LLM Buildable

Disk Space

Profile ID

BF16

1

False

TensorRT-LLM

True

14.97 GB

 
ac34857f8dcbd174ad524974248f2faf271bd2a0355643b2cf1490d0fe7787c2

BF16

2

False

TensorRT-LLM

True

14.97 GB

 
375dc0ff86133c2a423fbe9ef46d8fdf12d6403b3caa3b8e70d7851a89fc90dd

BF16

4

False

TensorRT-LLM

True

14.97 GB

 
54946b08b79ecf9e7f2d5c000234bf2cce19c8fee21b243c1a084b03897e8c95

BF16

8

False

TensorRT-LLM

True

14.97 GB

 
1d7b604f835f74791e6bfd843047fc00a5aef0f72954ca48ce963811fb6f3f09

Generic Model Profiles#

Precision

# of GPUs

LoRA

LLM Engine

Disk Space

Profile ID

BF16

1

False

vLLM

14.97 GB

 
4f904d571fe60ff24695b5ee2aa42da58cb460787a968f1e8a09f5a7e862728d

BF16

2

False

vLLM

14.97 GB

 
7fa4a5a68c0338f16aef61de94977acfdacb7cabd848d38c49c48d2f639f04b3

BF16

4

False

vLLM

14.97 GB

 
c84b2a068e56a551906563035ed77f88c88cbe1a63c6768fb2d4a9e0af1e67ba

BF16

8

False

vLLM

14.97 GB

 
f95be114df33dd6613105f76fd567a071ed3bd08232888a5ba2f0545a99dbd92