NVIDIA AI Blueprints are customizable reference workflows for building generative AI pipelines. Developers can use NVIDIA AI Blueprints to build multimodal RAG pipelines. The RAG Blueprint is built on NVIDIA NeMo Retriever models for continuously indexing multimodal documents for fast and accurate semantic search at enterprise scale. The VSS Blueprint ingests massive volumes of streaming or archival video for search, summarization, interactive Q&A, and event-trigger actions such as alerting.
Make Sense of Video Analytics by Integrating NVIDIA AI Blueprints | NVIDIA Technical Blog…includes an OCR engine and AI-powered extraction component for visual and text extraction and analysis. Architecture overview and data flow Extraction pipeline Nemotron Parse is integrated into the extraction pipeline, replacing…
…Skill cards centralize trust metadata, enabling developers and enterprise teams to assess compatibility, risks, and validation before deployment, while cryptographic signatures ensure authenticity and integrity beyond implied provenance. AI-generated content may…
…Integrated into Megatron Core and validated on NVIDIA DGX Hopper and Grace Blackwell platforms, Hybrid-EP demonstrated 514% throughput improvements over prior approaches in DeepSeek-V3, Megatron-FSDP, and Qwen 3 235B…
…Lovina shares her insights through research papers, open source projects, and writing on AI/ML security, adversarial machine learning, agentic AI systems, MLOps, LLMOps, AI/ML applications in various industries, TensorRT, deep…
…Maximize Quality With AI DLSS takes advantage of AI models that are continuously improved through ongoing training on NVIDIA supercomputers, providing better image quality and performance across more games and applications. The…
…and network counters to isolate where the slowdown occurred. Next steps for real-time observability The introduction of NCCL Inspector with Prometheus integration is designed to enhance network observability for AI workload…
…OSMO is purpose-built for physical AI and robotics workflows, which require dataset management, simulator integration, heterogeneous hardware, and multi-stage pipelines that SLURM isn’t designed to handle.
…dynamics simulations through dynamic batching and GPU-based integrators, supporting various MLIPs like MACE-MPA-0 and TensorNet, which enables concurrent processing and maximizes throughput. AI-generated content may summarize information incompletely…
…See NVIDIA Dynamo in Action How NVIDIA Dynamo Works Models are becoming larger and more integrated into AI workflows that require interaction with multiple models. Deploying these models at scale involves distributing…
…Developers can integrate CloudXR.js with various web frameworks and utilize provided WebGL and React sample clients for rapid prototyping, while production deployments are supported with Docker, WebSocket proxy configurations, and compatibility…