Model distillation for LLMs: A practical guide to smaller, faster AI

Model distillation is a crucial technique for optimizing large language models (LLMs) by transferring knowledge from a larger "teacher" model to a smaller "student" model, allowing for significant reductions in size and inference costs while maintaining most of the original model's accuracy. This process is advantageous for real-world applications, as it results in faster response times and lower operational costs, making it feasible to deploy on edge devices. The guide delineates the practical workflow of model distillation, which involves selecting a pre-trained teacher model, designing a smaller student model, generating soft labels, training with a combined loss, and validating performance. In addition to distillation, the document discusses other optimization techniques like quantization and pruning, highlighting their specific benefits and how they can be combined to maximize efficiency. Practical deployment scenarios demonstrate the real-world impact of these techniques, especially in applications requiring low latency and high efficiency, such as real-time chat apps and document processing pipelines. Recent advances in distillation methods, including the P-KD-Q sequence (Pruning → Knowledge Distillation → Quantization), emphasize the growing importance of reducing inference costs and optimizing LLM stacks with infrastructure-level enhancements like semantic caching and vector search.