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- 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.
- Ongoing research training transformer models at scale
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- Accelerated Computer Vision Lab (ACCV-Lab) is a systematic collection of packages with the common goal to facilitate end-to-end efficient training in the ADAS domain, each package offering tools & best practices for a specific aspect/task in this domain.
- 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.
- A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
- NVIDIA device plugin for Kubernetes
- 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.
- GPU accelerated decision optimization