Traditional design and engineering workflows in the manufacturing industry have long been characterized by a sequential, iterative approach that is often time-consuming and resource intensive. These conventional methods typically involve stages such as requirement gathering, conceptual design, detailed design, analysis, prototyping, and testing, with each phase dependent on the results of previous��
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Generative AI has revolutionized how people bring ideas to life, and agentic AI represents the next leap forward in this technological evolution. By leveraging sophisticated, autonomous reasoning and iterative planning, AI agents can tackle complex, multistep problems with remarkable efficiency. As AI continues to revolutionize industries, the demand for running AI models locally has surged.
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Filmmaking is an intricate and complex process that involves a diverse team of artists, writers, visual effects professionals, technicians, and countless other specialists. Each member brings their unique expertise to the table, collaborating to transform a simple idea into a captivating cinematic experience. From the initial spark of a story to the final cut, every step requires creativity��
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Today��s large language models (LLMs) achieve unprecedented results across many use cases. Yet, application developers often need to customize and tune these models to work specifically for their use cases, due to the general nature of foundation models. Full fine-tuning requires a large amount of data and compute infrastructure, resulting in model weights being updated.
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NVIDIA AI Workbench is now in beta, bringing a wealth of new features to streamline how enterprise developers create, use, and share AI and machine learning (ML) projects. Announced at SIGGRAPH 2023, NVIDIA AI Workbench enables developers to create, collaborate, and migrate AI workloads on their GPU-enabled environment of choice. To learn more, see Develop and Deploy Scalable Generative AI Models��
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Developing custom generative AI models and applications is a journey, not a destination. It begins with selecting a pretrained model, such as a Large Language Model, for exploratory purposes��then developers often want to tune that model for their specific use case. This first step typically requires using accessible compute infrastructure, such as a PC or workstation. But as training jobs get��
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Learn how AI is boosting creative applications for creators during NVIDIA GTC 2023, March 20-23.
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There are many benefits of GPUs in scaling AI, ranging from faster model training to GPU-accelerated fraud detection. While planning AI models and deployed apps, scalability challenges��especially performance and storage��must be accounted for. Regardless of the use case, AI solutions have four elements in common: Of these elements, data storage is often the most neglected during��
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If you��re wondering how an AI server is different from an AI workstation, you��re not the only one. Assuming strictly AI use cases with minimal graphics workload, obvious differences can be minimal to none. You can technically use one as the other. However, the results from each will be radically different depending on the workload each is asked to perform. For this reason, it��s important to��
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When production systems are not delivering expected levels of performance, it can be a challenging and time-consuming task to root-cause the issue(s). Especially in today��s complex environments, where the workload is comprised of many software components, libraries, etc, and rely on virtually all of the underlying hardware subsystems (CPU, memory, disk IO, network IO) to deliver maximum throughput.
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Personalizing treatments based on patients�� genetic variation will revolutionize how medicine cures diseases; but time to analysis has become a major bottleneck. Join Mehrzad Samadi, CEO of Parabricks on Thursday, March 1, 2018, from 11am-12pm PST as he discusses the use of GPU-acceleration to speed the analysis of DNA sequencing data. Parabricks has accelerated secondary analysis of sequencing��
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Boston-based AI startup Neurala has partnered with the Lindbergh Foundation in an effort to combat poaching in Africa using intelligent drones and deep learning. ��This is a terrific example of how AI technology can be a vital force for good,�� said Neurala CEO Max Versace. ��We��re thrilled to be working with the Lindbergh Foundation in this unique partnership, contributing our deep learning��
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Oliver Laslett, Post-Graduate Researcher at University of Southampton discusses how magnetic nanotechnology can be used to improve bio-medicine. Share your GPU-accelerated science with us: http://nvda.ly/Vpjxr Watch more scientists and researchers share how accelerated computing is #thepathforward: Watch Now ��
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