Techniques to reduce computational costs for LLMs include model pruning, quantization, knowledge distillation, and efficient architecture designs. Pruning removes less significant parameters, reducing the model size and the number of computations required for training and inference. For example, sparsity-based pruning focuses on retaining only the most important weights.
Quantization reduces numerical precision, such as using 8-bit integers instead of 32-bit floating-point numbers, which speeds up computations and decreases memory usage. Knowledge distillation involves training a smaller "student" model to mimic the behavior of a larger "teacher" model, achieving comparable performance with fewer resources.
Advanced architectures, such as sparse transformers and MoE (Mixture of Experts) models, further optimize computation by activating only a subset of model parameters during inference. These techniques, combined with hardware acceleration and optimized training frameworks like DeepSpeed, make LLMs more cost-effective for large-scale applications.