Support Matrix

Hardware

Unless specified otherwise, NVIDIA NIM for vision language models (VLMs) should, but are not guaranteed to, run on any NVIDIA GPU, provided the GPU has sufficient memory. They can also run on multiple homogeneous NVIDIA GPUs with sufficient aggregate memory and a CUDA compute capability of >= 7.0 (8.0 for bfloat16) unless otherwise specified. For more information, refer to Supported Models.

NVIDIA NIM for VLMs does not support NVIDIA Virtual GPU (vGPU) environments.

For information on the supported operating systems, drivers, and software, refer to the About Get Started page.

Supported Models

Mistral Medium 3.5 128B

Latest supported release tag: 2.0.6-variant

The following section lists the supported configurations for mistralai/mistral-medium-3.5-128b (NGC catalog page).

Generic Configuration

NIM for VLMs offers competitive performance through a custom vLLM backend. Any NVIDIA GPU with sufficient memory should be able to run this model, though this is not guaranteed.

The GPU Memory and Disk Space values are in GB.

GPU	GPU Memory	Precision	# of GPUs	Disk Space
H100-80GB-HBM3	80	FP8	4	124.45
H200	141	FP8	2,4	124.45
B200	192	FP8	2,4	124.45

Mistral Small 4

Latest supported release tag: 2.0.6-variant

The following section lists the supported configurations for mistralai/mistral-small-4-119b-2603 (NGC catalog page).

Generic Configuration

NIM for VLMs offers competitive performance through a custom vLLM backend. Any NVIDIA GPU with sufficient memory should be able to run this model, though this is not guaranteed.

The GPU Memory and Disk Space values are in GB.

GPU	GPU Memory	Precision	# of GPUs	Disk Space
H100-80GB-HBM3	80	FP8	4	113
H200	141	FP8	2,4	113
B200	192	FP8	2,4	113