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Dmitry Noranovich
Dmitry Noranovich

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NVIDIA GPUs for AI and Deep Learning inference workloads

NVIDIA GPUs optimized for inference are renowned for their ability to efficiently run trained AI models. These GPUs feature Tensor Cores that support mixed-precision operations, such as FP8, FP16, and INT8, boosting both performance and energy efficiency. Advanced architectural innovations, including Multi-Instance GPU (MIG) technology, ensure optimal resource allocation and utilization. Additionally, NVIDIA's robust software ecosystem simplifies AI model deployment, making these GPUs accessible for developers. Their scalability allows seamless integration into both data center and edge environments, enabling diverse AI applications. This combination of features makes NVIDIA GPUs a versatile and powerful solution for AI inference and, to some extent, training tasks.

Also, I shared my experience of building an AI Deep Learning workstation in the following article. If building a Deep Learning workstation is interesting for you, I'm building an app to aggregate GPU data from Amazon. In addition, you can listen to a podcast based on my article generated by NotebookLM.

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