Collaborative Research: SHF: Small: Building scalable GPU simulation and efficient GPU memory management for large machine learning acceleration
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Recent advancements in large machine learning models have demonstrated that increasing the number of parameters enhances computational precision and unlocks capabilities once deemed unattainable. This trend is exemplified by the rapid growth in model sizes, for instance, GPT-3 contained 175 billion parameters, while GPT-4 reportedly utilizes up to…
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