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How Meta trains large language models at scale
As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challenging shifts we’ve experienced is the sheer scale of co…
As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challenging shifts we’ve experienced is the sheer scale of computation required to train large language models (LLMs). This involves sophisticated algorithms that can allocate resources based on the needs of different jobs and dynamic scheduling to adapt to changing workloads. Once we’ve chosen a GPU and system, the task of placing them in a data center for optimal usage of resources (power, cooling, networking, etc.)
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