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TensorRT-LLM
PublicTensorRT 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.- Examples for Recommenders - easy to train and deploy on accelerated infrastructure.
- A unified library of SOTA model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
- Optimized primitives for collective multi-GPU communication
- NVIDIA Federated Learning Application Runtime Environment
- cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural networks. Also includes kernels for accelerated structure prediction.
- A Python framework for accelerated simulation, data generation and spatial computing.
- Ongoing research training transformer models 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.
- BioNeMo Framework: For building and adapting AI models in drug discovery at scale
- NVIDIA device plugin for Kubernetes