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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Generative AI, the ability of algorithms to process various types of inputs��such as text, images, audio, video, and code��and generate new content, is advancing at an unprecedented rate. While this technology is making significant strides across multiple industries, one sector that stands to benefit immensely is the Architecture, Engineering, and Construction (AEC) industry.
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NVIDIA TensorRT, an established inference library for data centers, has rapidly emerged as a desirable inference backend for NVIDIA GeForce RTX and NVIDIA RTX GPUs. Now, deploying TensorRT into apps has gotten even easier with prebuilt TensorRT engines. The newly released TensorRT 10.0 with weight-stripped engines offers a unique solution for minimizing the engine shipment size by reducing��
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In the fast-evolving landscape of generative AI, the demand for accelerated inference speed remains a pressing concern. With the exponential growth in model size and complexity, the need to swiftly produce results to serve numerous users simultaneously continues to grow. The NVIDIA platform stands at the forefront of this endeavor, delivering perpetual performance leaps through innovations across��
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In the dynamic realm of generative AI, diffusion models stand out as the most powerful architecture for generating high-quality images with text prompts. Models like Stable Diffusion have revolutionized creative applications. However, the inference process of diffusion models can be computationally intensive due to the iterative denoising steps required. This presents significant challenges��
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