Methods and tools for efficient training on a single GPU
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Transformers provides multiple inference optimization techniques to make models fast, affordable, and accessible. Options include alternative attention mechanisms for reduced memory traffic, code compilation for faster execution, and optimized kernels for throughput. Stack these techniques for maximum performance. Memory and speed are closely related but not the same. Shrinking your memory footprint makes a model “faster” because there is less data to move around. Pure speed optimizations don’t always reduce memory and sometimes increase usage. Choose the appropriate optimization based on…
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