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.
]]>NVIDIA today launched the NVIDIA RTX AI Toolkit, a collection of tools and SDKs for Windows application developers to customize, optimize, and deploy AI models for Windows applications. It’s free to use, doesn’t require prior experience with AI frameworks and development tools, and delivers the best AI performance for both local and cloud deployments. The wide availability of generative…
]]>Large language models (LLMs) are fundamentally changing the way we interact with computers. These models are being incorporated into a wide range of applications, from internet search to office productivity tools. They are advancing real-time content generation, text summarization, customer service chatbots, and question-answering use cases. Today, LLM-powered applications are running…
]]>Generative AI and large language models (LLMs) are changing human-computer interaction as we know it. Many use cases would benefit from running LLMs locally on Windows PCs, including gaming, creativity, productivity, and developer experiences. This post discusses several NVIDIA end-to-end developer tools for creating and deploying both text-based and visual LLM applications on NVIDIA RTX AI-ready…
]]>Retailers today have access to an abundance of video data provided by cameras and sensors installed in stores. Leveraging computer vision AI applications, retailers and software partners can develop AI applications faster while also delivering greater accuracy. These applications can help retailers: Building and deploying such highly efficient computer vision AI applications at scale…
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