NVIDIA Warp, a simulation computing framework, is now accessible to all developers.
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NVIDIA cuPyNumeric is a library that aims to provide a distributed and accelerated drop-in replacement for NumPy built on top of the Legate framework. It brings zero-code-change scaling to multi-GPU and multinode (MGMN) accelerated computing. cuPyNumeric 25.03 is a milestone update that introduces powerful new capabilities and enhanced accessibility for users and developers alike��
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Large language models (LLMs) are revolutionizing how developers code and how they learn to code. For seasoned or junior developers alike, today��s state-of-the-art models can generate Python scripts, React-based websites, and more. In the future, powerful AI models will assist developers in writing high-performance GPU code. This raises an important question: How can it be determined whether an LLM��
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AI is no longer just about generating text or images��it��s about deep reasoning, detailed problem-solving, and powerful adaptability for real-world applications in business and in financial, customer, and healthcare services. Available today, the latest Llama Nemotron Ultra reasoning model from NVIDIA delivers leading accuracy among open-source models across intelligence and coding benchmarks��
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Today, NVIDIA announced the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license. Originally developed within the Run:ai platform, KAI Scheduler is now available to the community while also continuing to be packaged and delivered as part of the NVIDIA Run:ai platform. This initiative underscores NVIDIA��s commitment to��
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As of 3/18/25, NVIDIA Triton Inference Server is now NVIDIA Dynamo. The explosion of AI-driven applications has placed unprecedented demands on both developers, who must balance delivering cutting-edge performance with managing operational complexity and cost, and AI infrastructure. NVIDIA is empowering developers with full-stack innovations��spanning chips, systems��
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During the 2024 OCP Global Summit, NVIDIA announced that it has contributed the NVIDIA GB200 NVL72 rack and compute and switch tray liquid cooled designs to the Open Compute Project (OCP). This post provides details about this contribution and explains how it increases the utility of current design standards to meet the high compute density demands of modern data centers.
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With the R515 driver, NVIDIA released a set of Linux GPU kernel modules in May 2022 as open source with dual GPL and MIT licensing. The initial release targeted datacenter compute GPUs, with GeForce and Workstation GPUs in an alpha state. At the time, we announced that more robust and fully-featured GeForce and Workstation Linux support would follow in subsequent releases and the NVIDIA Open��
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For manufacturing and industrial enterprises, efficiency and precision are essential. To streamline operations, reduce costs, and enhance productivity, companies are turning to digital twins and discrete-event simulation. Discrete-event simulation enables manufacturers to optimize processes by experimenting with different inputs and behaviors that can be modeled and tested step by step.
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Developing extended reality (XR) applications can be extremely challenging. Users typically start with a template project and adhere to pre-existing packaging templates for deploying an app to a headset. This approach creates a distinct bottleneck in the asset iteration pipeline. Updating assets inside an XR experience becomes completely dependent on how fast the developer can build, package��
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Smart cities are the future of urban living. Yet they can present various challenges for city planners, most notably in the realm of transportation. To be successful, various aspects of the city��from environment and infrastructure to business and education��must be functionally integrated. This can be difficult, as managing traffic flow alone is a complex problem full of challenges such as��
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NVIDIA PhysicsNeMo is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine learning (physics-ML) models. PhysicsNeMo is available as OSS (Apache 2.0 license) to support the growing physics-ML community. The latest PhysicsNeMo software update, version 23.05, brings together new capabilities��
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Recent years have seen a proliferation of large language models (LLMs) that extend beyond traditional language tasks to generative AI. This includes models like ChatGPT and Stable Diffusion. As this generative AI focus continues to grow, there is a rising need for a modern machine learning (ML) infrastructure that makes scalability accessible to the everyday practitioner.
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This version 23.05 update to the NVIDIA PhysicsNeMo platform expands support for physics-ML and provides minor updates.
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Robots are increasing in complexity, with a higher degree of autonomy, a greater number and diversity of sensors, and more sensor fusion-based algorithms. Hardware acceleration is essential to run these increasingly complex workloads, enabling robotics applications that can run larger workloads with more speed and power efficiency. The mission of NVIDIA Isaac ROS has always been to empower��
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Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to��
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