Summary Training localized multilingual large language models (LLMs) is crucial for AI systems to understand and communicate in diverse languages. NVIDIA NeMo provides a comprehensive platform for developing custom generative AI, including tools for training, retrieval-augmented generation, guardrailing, and data curation. This article explores the best practices for adding new language support to base LLMs using NeMo, focusing on training and merging a multilingual tokenizer and performing continual pretraining. Building Localized Multilingual LLMs with NVIDIA NeMo In today’s globalized world, the ability of AI systems to understand and communicate in diverse languages is increasingly important....

Summary Training object detection models for visual inspection tasks can be challenging due to the need for large, diverse datasets. Synthetic data offers a solution by allowing for the generation of photorealistic images with perfect annotations. This article explores how to train an object detection model using synthetic data with NVIDIA Omniverse Replicator, highlighting the benefits of synthetic data and providing a step-by-step guide on how to generate and use synthetic data for model training....

Summary NVIDIA has introduced AutoMate, a groundbreaking framework designed to train robots for assembly tasks across diverse geometries. This innovative approach leverages simulation and learning methods to bridge the gap between simulation and real-world applications. AutoMate is the first simulation-based framework to train both specialist and generalist robotic assembly skills, demonstrating zero-shot sim-to-real transfer of skills. This means that capabilities learned in simulation can be directly applied in real-world settings without additional adjustments....

Summary: Financial analysis is a critical component of investment decisions, requiring the synthesis and interpretation of vast amounts of data. Traditional methods often fall short in extracting nuanced insights from financial documents. NVIDIA NIM offers a solution by leveraging advanced AI technologies to accelerate workflows, uncover hidden insights, and enhance decision-making processes in the financial services industry. Revolutionizing Financial Analysis with AI Financial analysis is a cornerstone of investment decisions, involving the meticulous examination of financial data to gain a competitive edge....

Summary: Telecom networks face significant challenges in managing and optimizing AI workloads. The integration of AI into these networks can help solve these issues by making them smarter and faster. Nvidia’s AI-RAN platform is at the forefront of this effort, leveraging billions of data points to create algorithms that determine optimal network adjustments and predict real-time capacity. This technology allows telcos to run third-party AI applications at the network’s edge, enabling AI-as-a-Service (AIaaS) for enterprise customers....

Treating Brain Diseases with Brain-Machine Interactive Neuromodulation and NVIDIA Jetson Summary Brain-Machine Interactive Neuromodulation (BMINT) is a groundbreaking technique that combines neural sensing, machine learning algorithms, and real-time electrical stimulation to treat brain diseases such as Parkinson’s, epilepsy, and depression. The BMINT research tool, powered by NVIDIA Jetson, has achieved significant advancements in precision and personalized electronic medicine. This article explores the main ideas behind BMINT and its potential to revolutionize brain disease treatment....

Summary This article explores how to tune and deploy large language models (LLMs) with NVIDIA TensorRT-LLM, focusing on the use of low-rank adaptation (LoRA) techniques. It covers the benefits of using LoRA adapters, how to deploy them with TensorRT-LLM, and provides practical insights into optimizing performance. Tuning and Deploying LoRA LLMs with NVIDIA TensorRT-LLM NVIDIA TensorRT-LLM is a powerful tool for optimizing large language model (LLM) inference. One of the key features that makes it stand out is its support for low-rank adaptation (LoRA) techniques....

Turning Machine Learning into Federated Learning in Minutes with NVIDIA FLARE 2.4 Summary Federated learning is a machine learning technique that allows organizations to train AI models on decentralized data without the need to centralize or share that data. NVIDIA FLARE 2.4 is a domain-agnostic, open-source, and extensible SDK that enables researchers and data scientists to adapt existing ML/DL workflows to a federated paradigm. This article explores how NVIDIA FLARE 2....

Understanding Overhead and Latency in NVIDIA Nsight Systems Summary: NVIDIA Nsight Systems is a powerful tool for analyzing and optimizing the performance of applications running on NVIDIA GPUs. This article delves into the visualization of overhead and latency in Nsight Systems, providing insights into how developers can use this tool to identify and address performance bottlenecks in their applications. What is NVIDIA Nsight Systems? NVIDIA Nsight Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, identify optimization opportunities, and tune performance to scale efficiently across CPUs and GPUs....

Unlocking Faster Image Generation in Stable Diffusion Web UI with NVIDIA TensorRT Summary Stable Diffusion, an open-source generative AI model, can be significantly accelerated using NVIDIA TensorRT. This article explores how TensorRT can enhance the performance of Stable Diffusion, particularly in the Web UI, and provides insights into its implementation and benefits. Understanding Stable Diffusion and NVIDIA TensorRT Stable Diffusion is a powerful tool for generating images based on text descriptions....