The NVIDIA CUDA-Q platform is designed to streamline software and hardware development for hybrid, accelerated quantum supercomputers. Users can write code once, test it on any QPU or simulator, and accelerate all parts of the workflow. This liberates time for achieving scientific breakthroughs rather than waiting for results. CUDA-Q v0.10 has more features and increased performance��
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Microsoft, in collaboration with NVIDIA, announced transformative performance improvements for the Meta Llama family of models on its Azure AI Foundry platform. These advancements, enabled by NVIDIA TensorRT-LLM optimizations, deliver significant gains in throughput, reduced latency, and improved cost efficiency, all while preserving the quality of model outputs. With these improvements��
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NVIDIA announced at GTC 2025 the release of NVIDIA Holoscan 3.0, the real-time AI sensor processing platform. This latest version provides dynamic flow control, empowering developers to design more robust, scalable, and efficient systems. With physical AI rapidly evolving, Holoscan 3.0 is built to adapt, making it easier than ever to tackle the challenges of today��s dynamic environments.
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NVIDIA Virtual GPU (vGPU) technology unlocks AI capabilities within Virtual Desktop Infrastructure (VDI), making it more powerful and versatile than ever before. By powering AI-driven workloads across virtualized environments, vGPU boosts productivity, strengthens security, and optimizes performance. The latest software release empowers businesses and developers to push innovation further��
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For years, advancements in AI have followed a clear trajectory through pretraining scaling: larger models, more data, and greater computational resources lead to breakthrough capabilities. In the last 5 years, pretraining scaling has increased compute requirements at an incredible rate of 50M times. However, building more intelligent systems is no longer just about pretraining bigger models.
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The wireless industry stands at the brink of a transformation, driven by the fusion of AI with advanced 5G and upcoming 6G technologies that promise unparalleled speeds, ultra-low latency, and seamless connectivity for billions of AI-powered endpoints. 6G specifically will be AI-native, enabling integrated sensing and communications, supporting immersive technologies like extended reality and��
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The growing volume and complexity of medical data��and the pressing need for early disease diagnosis and improved healthcare efficiency��are driving unprecedented advancements in medical AI. Among the most transformative innovations in this field are multimodal AI models that simultaneously process text, images, and video. These models offer a more comprehensive understanding of patient data than��
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Generative chemistry with AI has the potential to revolutionize how scientists approach drug discovery and development, health, and materials science and engineering. Instead of manually designing molecules with ��chemical intuition�� or screening millions of existing chemicals, researchers can train neural networks to propose novel molecular structures tailored to the desired properties.
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NVIDIA Parabricks is a scalable genomics analysis software suite that solves omics challenges with accelerated computing and deep learning to unlock new scientific breakthroughs. Released at NVIDIA GTC 2025, NVIDIA Parabricks v4.5 supports the growing quantity of data by including support for the latest NVIDIA GPU architectures, and improved alignment and variant calling with the��
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NVIDIA DGX Cloud Serverless Inference is an auto-scaling AI inference solution that enables application deployment with speed and reliability. Powered by NVIDIA Cloud Functions (NVCF), DGX Cloud Serverless Inference abstracts multi-cluster infrastructure setups across multi-cloud and on-premises environments for GPU-accelerated workloads. Whether managing AI workloads��
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As AI capabilities advance, understanding the impact of hardware and software infrastructure choices on workload performance is crucial for both technical validation and business planning. Organizations need a better way to assess real-world, end-to-end AI workload performance and the total cost of ownership rather than just comparing raw FLOPs or hourly cost per GPU.
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The future of MedTech is robotic��hospitals will be fully automated, with AI-driven surgical systems, robotic assistants, and autonomous patient care transforming healthcare as we know it. Building AI-driven robotic systems poses several key challenges. Integrating data collection with expert insights is one. Creating detailed biomechanical simulations for realistic anatomy, sensors��
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With the rise of physical AI, video content generation has surged exponentially. A single camera-equipped autonomous vehicle can generate more than 1 TB of video daily, while a robotics-powered manufacturing facility may produce 1 PB of data daily. To leverage this data for training and fine-tuning world foundation models (WFMs), you must first process it efficiently.
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Enterprises are generating and storing more multimodal data than ever before, yet traditional retrieval systems remain largely text-focused. While they can surface insights from written content, they aren��t extracting critical information embedded in tables, charts, and infographics��often the most information-dense elements of a document. Without a multimodal retrieval system��
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The world of robotics is undergoing a significant transformation, driven by rapid advancements in physical AI. This evolution is accelerating the time to market for new robotic solutions, enhancing confidence in their safety capabilities, and contributing to the powering of physical AI in factories and warehouses. Announced at GTC, Newton is an open-source, extensible physics engine developed��
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With the release of NVIDIA AgentIQ��an open-source library for connecting and optimizing teams of AI agents��developers, professionals, and researchers can create their own agentic AI applications. This tutorial shows you how to develop apps in AgentIQ through an example of AI code generation. We build a test-driven coding agent using LangGraph and reasoning models to scale test-time computation.
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Organizations are embracing AI agents to enhance productivity and streamline operations. To maximize their impact, these agents need strong reasoning abilities to navigate complex problems, uncover hidden connections, and make logical decisions autonomously in dynamic environments. Due to their ability to tackle complex problems, reasoning models have become a key part of the agentic AI��
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As agentic AI systems evolve and become essential for optimizing business processes, it is crucial for developers to update them regularly to stay aligned with ever-changing business and user needs. Continuously refining these agents with AI and human feedback ensures that they remain effective and relevant. NVIDIA NeMo microservices is a fully accelerated, enterprise-grade solution designed��
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NVIDIA announced the release of NVIDIA Dynamo today at GTC 2025. NVIDIA Dynamo is a high-throughput, low-latency open-source inference serving framework for deploying generative AI and reasoning models in large-scale distributed environments. The framework boosts the number of requests served by up to 30x, when running the open-source DeepSeek-R1 models on NVIDIA Blackwell.
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NVIDIA announced world-record DeepSeek-R1 inference performance at NVIDIA GTC 2025. A single NVIDIA DGX system with eight NVIDIA Blackwell GPUs can achieve over 250 tokens per second per user or a maximum throughput of over 30,000 tokens per second on the massive, state-of-the-art 671 billion parameter DeepSeek-R1 model. These rapid advancements in performance at both ends of the performance��
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The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don��t get proper training and face testing risks due to poor generalization, limited exposure to real-world variations, and unpredictable behavior in edge cases. Collecting massive real-world datasets for��
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Physical AI models enable robots to autonomously perceive, interpret, reason, and interact with the real world. Accelerated computing and simulations are key to developing the next generation of robotics. Physics plays a crucial role in robotic simulation, providing the foundation for accurate virtual representations of robot behavior and interactions within realistic environments.
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