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621 repositories
- BioNeMo Framework: For building and adapting AI models in drug discovery at scale
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- CUDA Core Compute Libraries
- TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
- A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.
- Ongoing research training transformer models at scale
- NVIDIA Federated Learning Application Runtime Environment
- Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
- NeMo Retriever extraction is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever extraction uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications.
- Optimized primitives for collective multi-GPU communication
- 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, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
- NeMo text processing for ASR and TTS
- GPU accelerated decision optimization