Application Layer Software#
Application Layer Components: The application layer provides specialized SDKs, frameworks, optimized microservices, and pre-trained models. It includes development and deployment tools such as Triton Inference Server, TensorFlow, and PyTorch, along with optimized libraries for deep learning, data science, and machine learning.
AI Solution Development: These components enable building and deploying AI solutions — generative AI, AI agents, physical AI, and domain-specific applications. By separating the infrastructure layer (which is versioned) from the application layer, NVIDIA AI Enterprise ensures that foundational updates and improvements do not disrupt the development and deployment of AI applications. For infrastructure layer components, see Infrastructure Layer Software.
Release Distribution: Application software is distributed through three release branch types: Feature Branch (FB) for latest innovation, Production Branch (PB) for production stability, and Long-Term Support Branch (LTSB) for regulated environments requiring 36 months of API stability. For more information, see Lifecycle Policy.
Software Components#
NVIDIA AI Enterprise includes the following NVIDIA software components. For infrastructure layer components, see Infrastructure Layer Software.
Attention
This is a representative list of key software components and is not exhaustive. The full set of supported software evolves with each release. Refer to the individual product documentation and release notes for the most current information.
Component |
Description |
NGC Catalog |
Documentation |
|---|---|---|---|
Inference & Deployment |
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NVIDIA NIM |
Optimized microservices for accelerating foundation model deployment on any cloud or data center. Includes production-grade runtimes, security updates, and API references. |
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NVIDIA TensorRT |
High-performance deep learning inference optimizer and runtime for production deployment of trained models. |
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NVIDIA Triton Inference Server |
Multi-framework inference server with optimized backends for deploying AI models at scale. Supports multiple model formats and dynamic batching. |
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AI Frameworks & Libraries |
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CUDA Deep Learning |
Container bundle with GPU-accelerated deep learning libraries including cuDNN, NCCL, and CUDA runtime for training and inference workloads. |
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NVIDIA NeMo |
End-to-end platform for building, customizing, and deploying generative AI models including LLMs, multimodal, speech AI, and vision. |
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PyTorch |
NVIDIA-optimized PyTorch container with GPU-accelerated deep learning and data science libraries. |
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RAPIDS |
GPU-accelerated data science libraries for data preparation, machine learning, and graph analytics. |
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RAPIDS Accelerator for Apache Spark |
GPU acceleration plugin for Apache Spark 3 data science pipelines and AI model training. |
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Domain SDKs & Toolkits |
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NVIDIA Clara Parabricks |
GPU-accelerated computational genomics toolkit for secondary and tertiary analysis of next-generation sequencing data. |
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NVIDIA DeepStream SDK |
Streaming analytics toolkit for building AI-powered video and sensor data applications at scale. |
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NVIDIA Holoscan SDK |
Sensor processing platform for building real-time AI pipelines in healthcare, industrial inspection, and edge applications. |
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NVIDIA Morpheus |
Cybersecurity AI framework for real-time threat detection and digital fingerprinting at data center scale. |
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NVIDIA Riva |
GPU-accelerated speech AI and conversational AI services for automatic speech recognition (ASR), text-to-speech (TTS), and natural language processing. |
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NVIDIA TAO Toolkit |
AI model adaptation toolkit for fine-tuning pre-trained models with custom data, enabling transfer learning workflows. |
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Pre-trained Models |
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Production-Ready Pre-trained Models |
Pre-trained AI models that simplify and accelerate development by eliminating the need to build from scratch. |
Varies by model. Refer to the documentation links on the product pages on NGC Catalog. |
Release Branches and Lifecycle#
Application software is distributed through three release branch types — Feature Branch (FB), Production Branch (PB), and Long-Term Support Branch (LTSB) — each offering a different balance of innovation speed, API stability, and support duration.
Lifecycle Policy — Defines each branch type, support periods, and update cadence.
Choosing the Right Release Branch — Decision guide with comparison table and industry scenarios.
Application Software Releases — Active and archived release branches.