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Introducing DBRX: The Future of Language Models // [Exclusive] Databricks Roundtable

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Optimizing Language Model Inference

The chapter explores optimizing PyTorch FSDP for language models, emphasizing tools like PyTorch profiler and memory profile for benchmarking and the complexities of measuring inference latency and throughput. It delves into leveraging customized inference web servers and realistic workload simulations to enhance model serving efficiency. The discussion also focuses on the evolution of language model scaling, data quality considerations, and challenges of distributing data at scale on cloud platforms for training machine learning models.

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