This post is part of a series about optimizing end-to-end AI. The performance of AI models is heavily influenced by the precision of the computational resources being used. Lower precision can lead to faster processing speeds and reduced memory usage, while higher precision can contribute to more accurate results. Finding the right balance between precision and performance is crucial for…
]]>This post is part of a series about optimizing end-to-end AI. While NVIDIA hardware can process the individual operations that constitute a neural network incredibly fast, it is important to ensure that you are using the tools correctly. Using the respective tools such as ONNX Runtime or TensorRT out of the box with ONNX usually gives you good performance, but why settle for good performance…
]]>This post is the first in a series about optimizing end-to-end AI. The great thing about the GPU is that it offers tremendous parallelism; it allows you to perform many tasks at the same time. At its most granular level, this comes down to the fact that there are thousands of tiny processing cores that run the same instruction at the same time. But that is not where such parallelism stops.
]]>NVIDIA Nsight Deep Learning Designer is a new tool that helps ease the process of performant model design. DL Designer provides valuable insights into the structure of the model, and how well it performs on NVIDIA hardware. Models can be created with a user-friendly, drag-and-drop interface that features nodes for all of the commonly used operators available in the most popular deep learning…
]]>Every year, clever researchers introduce ever more complex and interesting deep learning models to the world. There is of course a big difference between a model that works as a nice demo in isolation and a model that performs a function within a production pipeline. This is particularly pertinent to creative apps where generative models must run with low latency to generate or enhance image…
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