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Overcoming Cross-Platform Deployment Hurdles in the Age of AI Processing Units

Unite.AI

Graphic Processing Units (GPUs): Originally designed for graphics rendering, GPUs have become essential for AI computations due to their parallel processing capabilities. GPUs use CUDA (Compute Unified Device Architecture), allowing developers to write software in C or C++ for efficient parallel computation.

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Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

Unite.AI

Projects like cuDNN , cuBLAS , and NCCL are available as open-source libraries, enabling researchers and developers to leverage the full potential of CUDA for their deep learning. Installation When setting AI development, using the latest drivers and libraries may not always be the best choice. xx) supports CUDA 12.3,

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The Story of Modular

Mlearning.ai

In the first part of this blog, we are going to explore how Modular came into existence, who are it’s founding members, and what they have to offer to the AI community. Android ML  — It brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. Read more about it here.