Lilian Weng • 1/10/2023

Large Transformer Model Inference Optimization

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This technical article details the challenges of running inference for large transformer models, such as high memory footprint and quadratic attention scaling. It provides an overview of optimization methods including model parallelism, memory offloading, smart batching, and network compression techniques like pruning, quantization, and knowledge distillation to reduce latency and computational cost.

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#Attention Mechanism #Kv Cache #Transformer Models
Large Transformer Model Inference Optimization

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