As generative AI experiences rapid growth, the community has stepped up to foster this expansion in two significant ways: swiftly publishing state-of-the-art foundational models, and streamlining their integration into application development and production. NVIDIA is aiding this effort by optimizing foundation models to enhance performance, allowing enterprises to generate tokens faster…
]]>The incredible advances of accelerated computing are powered by data. The role of data in accelerating AI workloads is crucial for businesses looking to stay ahead of the curve in the current fast-paced digital environment. Speeding up access to that data is yet another way that NVIDIA accelerates entire AI workflows. NVIDIA DGX Cloud caters to a wide variety of market use cases.
]]>NVIDIA Base Command Platform provides the capabilities to confidently develop complex software that meets the performance standards required by scientific computing workflows. The platform enables both cloud-hosted and on-premises solutions for AI development by providing developers with the tools needed to efficiently configure and manage AI workflows. Integrated data and user management simplify…
]]>This post was originally published in August 2019 and has been updated for NVIDIA TensorRT 8.0. Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. Large-scale language models (LSLMs) such as BERT, GPT-2, and XL-Net have brought exciting leaps in accuracy for many natural language processing…
]]>Large scale language models (LSLMs) such as BERT, GPT-2, and XL-Net have brought about exciting leaps in state-of-the-art accuracy for many natural language understanding (NLU) tasks. Since its release in Oct 2018, BERT1 (Bidirectional Encoder Representations from Transformers) remains one of the most popular language models and still delivers state of the art accuracy at the time of writing2.
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