LLMs are optimized for memory usage through techniques like model quantization, parameter sharing, and activation checkpointing. Quantization reduces the precision of numerical computations, such as using 8-bit integers instead of 32-bit floats, which lowers memory requirements without significantly affecting accuracy.
Parameter sharing involves reusing the same parameters across multiple layers or tasks, which reduces the number of unique weights stored in memory. This is commonly used in transformer architectures to improve efficiency. Activation checkpointing saves memory during training by storing only a subset of intermediate activations and recomputing them during the backward pass, trading compute for reduced memory consumption.
Memory optimization also includes leveraging hardware-specific features like GPU memory hierarchies and utilizing efficient data formats. These approaches ensure that LLMs can handle large-scale models and datasets without exceeding hardware limitations, enabling scalable and cost-effective deployment.