LLMs balance accuracy and efficiency through techniques like model pruning, quantization, and efficient architecture design. Pruning removes less significant parameters from the model, reducing its size and computational requirements without significantly impacting accuracy.
Quantization reduces the precision of computations, such as converting 32-bit floating-point numbers to 16-bit or 8-bit formats. This lowers memory usage and speeds up inference while maintaining acceptable accuracy. Modern LLM architectures, such as transformer variants, also optimize efficiency by using sparse attention mechanisms or other innovations that reduce unnecessary computations.
Developers fine-tune pre-trained models on specific tasks to improve accuracy without requiring excessive training. They also leverage techniques like distillation, where a smaller model learns from a larger one, achieving comparable performance with reduced complexity. These strategies allow LLMs to meet the varying demands of accuracy and efficiency in real-world applications.