Optimizing Inference Efficiency in Large-Scale Transformer Models

Large-scale transformer models have advanced artificial intelligence across multiple domains, yet their substantial computational costs during inference present a major obstacle to real-world deployment at scale. The article surveys multiple techniques for reducing inference costs in both processing speed and memory consumption. Knowledge distillation enables training of compact models that replicate larger pre-trained models' behavior, dramatically cutting inference expenses. DistilBERT demonstrates this approach by achieving 40% parameter reduction while maintaining 97% of the original model's performance and running 71% faster. Quantization reduces the precision of network weights and activations, lowering memory requirements and accelerating computations. Methods like GPTQ achieve 3-4 bit weight precision with minimal performance loss, while SmoothQuant employs mathematical transformations to smooth extreme features and enable simultaneous quantization of weights and activations. Pruning removes non-essential weights from models, reducing network size while preserving performance. Combined with quantization-aware training and other compression approaches, pruning contributes to comprehensive inference optimization strategies.