NVIDIA Corporation
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- CUDA Core Compute Libraries
- 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.
- TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support 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 performant way.
- Differentiable signal processing on the sphere for PyTorch
- A Python framework for accelerated simulation, data generation and spatial computing.
- Ongoing research training transformer models at scale
- 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.
- BioNeMo Framework: For building and adapting AI models in drug discovery at scale
- 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.
- AIStore: scalable storage for AI applications
- GPU accelerated decision optimization
- Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows