Summary NVIDIA and Microsoft have collaborated to enhance AI workloads with GPU acceleration on Windows 365 Cloud PCs. This integration allows users to perform complex and graphics-intensive tasks without the need for separate physical infrastructure. The collaboration marks a significant advancement in cloud computing, enabling enhanced workflow efficiencies and transforming how businesses and developers approach AI applications. Powering AI-Augmented Workloads with NVIDIA and Windows 365 The digital landscape is evolving rapidly, and AI-augmented workflows are at the forefront of this transformation....

Advancing Autonomous Operations with AI-Driven Solutions Summary The energy industry is witnessing a significant shift towards autonomous operations, driven by advancements in artificial intelligence (AI) and simulation technologies. AVEVA and NVIDIA have partnered to create an AI-driven solution that provides autonomous plant control through steady-state and complex transient conditions. This article explores how this collaboration is advancing autonomous operations and what benefits it brings to the energy sector. The Challenge of Autonomous Operations Autonomous operations in the energy industry involve managing complex systems with minimal human intervention....

How AI is Revolutionizing Neuroscience Research with Visual Question Answering and Multimodal Retrieval Summary Neuroscience research is witnessing a significant leap forward thanks to the integration of AI technologies, particularly in visual question answering (VQA) and multimodal retrieval. The Indian Institute of Technology Madras (IIT Madras) Brain Centre, in collaboration with NVIDIA, has developed a groundbreaking framework that combines VQA models with large language models (LLMs) to make brain imaging data more accessible and understandable....

Deploying Fine-Tuned AI Models with NVIDIA NIM: A Step-by-Step Guide Summary Deploying fine-tuned AI models is crucial for delivering value with enterprise generative AI applications. NVIDIA NIM offers prebuilt, performance-optimized inference microservices for the latest AI foundation models, including seamless deployment of models customized using parameter-efficient fine-tuning (PEFT) and supervised fine-tuning (SFT). This article explores how to rapidly deploy NIM microservices for fine-tuned models, highlighting the benefits and steps involved in the process....

Unlocking High Performance on NVIDIA GPUs: Full-Stack Optimizations for Llama 3.2 Summary: NVIDIA has optimized the Llama 3.2 collection of models to deliver high throughput and low latency across millions of GPUs worldwide. This article explores the full-stack optimizations that enable high performance on NVIDIA GPUs, from data centers to local workstations and edge devices. Delivering High Throughput and Low Latency The Llama 3.2 series of vision language models (VLMs) come in 11B parameter and 90B parameter variants, supporting both text and image inputs....

Summary: Prostate cancer is a significant health concern for men, with over 300,000 new cases diagnosed each year in the U.S. Recent advancements in AI research have led to the development of a model that can quickly analyze MRIs to accurately predict how prostate cancer tumors may develop and potentially metastasize over time. This breakthrough could revolutionize the way clinicians diagnose and treat prostate cancer, offering a more personalized and effective approach to patient care....

Summary Digital twins are virtual replicas of physical environments, systems, or processes that integrate real-time data from sensors and other sources to simulate behavior and predict outcomes. By connecting real-time IoT data to digital twins, organizations can enhance remote monitoring, improve operational efficiency, and make informed decisions. This article explores how digital twins, powered by platforms like NVIDIA Omniverse, can revolutionize industries by providing dynamic, photorealistic 3D environments for simulation, analysis, and collaboration....

Summary: The US power grid faces significant challenges in optimizing its operations due to the complexity of large-scale nonlinear optimization problems. The NVIDIA cuDSS library, combined with high-memory GPUs like the NVIDIA Grace Hopper Superchip, offers a breakthrough in solving these problems efficiently. This article explores how researchers from MIT, MINES Paris – PSL, and ANL have developed nonlinear optimization algorithms and solvers using NVIDIA tools to achieve significant advancements in power systems optimization (PSO)....

Unlocking Quantum Supercomputing: NVIDIA’s CUDA-QX Libraries Summary: NVIDIA has introduced CUDA-QX libraries, a significant advancement in quantum supercomputing. These libraries integrate quantum processing units (QPUs) with traditional CPU and GPU architectures, aiming to tackle some of the world’s most challenging computational problems. This article explores the key features and applications of CUDA-QX libraries, highlighting their potential to revolutionize quantum computing. Revolutionizing Quantum Supercomputing NVIDIA’s CUDA-QX libraries are part of a broader effort to combine AI supercomputing with quantum computing capabilities....

Summary NVIDIA cuPyNumeric is a groundbreaking library that enables scientists and researchers to scale their NumPy-based programs effortlessly from laptops to supercomputers without any code changes. This library leverages the power of GPU acceleration to handle large datasets efficiently, making it a crucial tool for data-intensive fields such as astronomy, materials science, and machine learning. Scaling NumPy from Laptops to Supercomputers Python is the most common programming language for data science, machine learning, and numerical computing....