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dgx-spark-playbooks
Public- NeMo text processing for ASR and TTS
- 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
- CUDA Core Compute Libraries
- Documentation repository for NVIDIA Cloud Native Technologies
- GPU accelerated decision optimization
- 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.
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- LLM KV cache compression made easy
- 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.
- AIStore: scalable storage for AI applications
- 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.