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3 articles
This article explains how GPU acceleration, particularly NVIDIA's RTX technology, enables faster AI processing by leveraging parallel computing architectures. It covers the technical foundations of Tensor Cores, mixed-precision training, and their impact on machine learning workflows.
Learn how fused kernels and automatic mixed precision (AMP) techniques, such as those in NVIDIA Apex and PyTorch's torch.amp, can dramatically accelerate transformer training by optimizing computational efficiency and reducing memory overhead.
This article explains how to implement NVIDIA's Transformer Engine with mixed-precision, FP8 support, benchmarking, and fallback execution for optimizing transformer model performance.
