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- Ongoing research training transformer models at scale
tinylinux-scripts
Publictorch-harmonics
PublicDifferentiable signal processing on the sphere for PyTorch- LLM KV cache compression made easy
TensorRT-LLM
PublicTensorRT 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.- 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.
- A Python framework for accelerated simulation, data generation and spatial computing.
- Kubernetes enhancements for Network Topology Aware Gang Scheduling & Autoscaling
- CUDA Core Compute Libraries
- 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
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- GPU accelerated decision optimization
- NVIDIA Federated Learning Application Runtime Environment
- Documentation repository for NVIDIA Cloud Native Technologies