About NVIDIA NIM for Multimodal Safety#
NVIDIA NIM offers prebuilt containers for multimodal safety models that can be used to safeguard AI applications — or any application that needs to understand and generate multimodal content. Each NIM consists of a container and a model and uses a CUDA-accelerated runtime for all NVIDIA GPUs, with special optimizations available for many configurations. Whether on-premises or in the cloud, NIM is the fastest way to achieve accelerated generative AI inference at scale.
Enterprise-Ready Features#
NIM abstracts away model inference internals such as execution engine and runtime operations. NVIDIA NIM for Visual GenAI offers the following enterprise-ready features:
High Performance NIM is optimized for high-performance deep learning inference with NVIDIA TensorRT and NVIDIA Triton Inference Server.
Scalable Deployment that is performant and can quickly and seamlessly scale from a few users to millions.
Enterprise-Grade Security emphasizes security by constantly monitoring and patching CVEs in our stack and conducting internal penetration tests.
Architecture#
Each Multimodal Safety NIM packages a model for the content moderation, such as hive/ai-generated-image-detection, into a Docker container image. All Multimodal Safety NIM containers are accelerated with NVIDIA TritonTM Inference Server.
The containers can run on any NVIDIA GPU with sufficient GPU memory. The model is not packaged as part of the image, but can be downloaded automatically from cloud storage. NVIDIA produces several model profiles that are optimized for popular data-center GPU models. Refer to the Support Matrix for more information.
Because the container can download the model from cloud storage, NVIDIA recommends caching the model on local storage to improve container startup times. In addition, because each container is built from common base images, downloading additional containers or newer images is faster than the initial download.
A security scan report is available for each container. The scan report provides a security rating of that image, a breakdown of CVE severity by package, and links to detailed information on CVEs.
Applications#
Each Multimodal Safety NIM is designed to address a particular content moderation scenario leveraging the AI models. For a full list of supported models, see Supported Models.
The potential uses are broad including, but not limited to:
AI Generated Image Detection#
Art and Design Verification: verify the authenticity of artwork and detect AI-generated forgeries.
Social Media Moderation: detect and flag AI-generated images that may be used to spread misinformation or propaganda.
Cybersecurity: detect AI-generated images used in phishing attacks or other types of cyber threats
Deepfake Image Detection#
Profile Verification: verify the authenticity of user profile pictures, helping to prevent catfishing and other forms of online harassment.
Insurance Claims Verification: verify the authenticity of images submitted as part of insurance claims, helping to prevent fraudulent claims and reduce costs.
Advertising and Marketing: verify the authenticity of images used in ad campaigns, ensuring that they are not using manipulated or fake content that could damage their brand reputation