Unlike fifty years ago, the techniques of artificial intelligence and deep learning, along with advances in hardware platforms and massively parallel systems, have put the capabilities of quality language translation at everyone’s fingertips. Making this possible are the latest developments in neural networks and deep learning systems, and, in
particular, a neural network architecture called transformers. Researchers have shown that transformer networks are particularly well suited for parallelization on GPU-based systems. These networks outperform traditional machine translation models and are very capable of producing high-quality translations.
The problem is that transformer networks require very large amounts of GPU memory, well beyond what you find in most entry level deep learning platforms. How much GPU memory? Which GPU models should you choose? And which training parameter settings give the best quality translations? Even with the latest advances in training transformer models, little has been published regarding the GPUs that should be used for this task. And that’s
what is discussed in this whitepaper.
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