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- A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
- CUDA Core Compute Libraries
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- AIStore: scalable storage for AI applications
- A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
- LLM KV cache compression made easy
- BioNeMo Framework: For building and adapting AI models in drug discovery at scale
- CUDA Templates for Linear Algebra Subroutines
- A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
- Scalable data pre processing and curation toolkit for LLMs
NeMo-Aligner
Public- NVIDIA device plugin for Kubernetes
- TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.
- TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization, pruning, distillation, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed on NVIDIA GPUs.