In the quest to reduce LLM inference costs, model distillation stands out as a premier strategy. Distillation involves training a smaller, more efficient model (the student) to replicate the behavior of a larger, more powerful model (the teacher). This method not only significantly reduces the computational resources required for inference but also maintains a high level of performance. Research shows that distilled models can achieve up to 90% of the performance of their larger counterparts while requiring substantially fewer resources (Hinton et al., 2015).

Quantization further enhances this cost-efficiency by reducing the precision of the model weights, which can lead to a reduction in memory usage and increased inference speed without a significant drop in quality. For instance, quantized models can achieve up to 4x faster inference speeds (Jacob et al., 2018).

While techniques like prompt caching and batching are beneficial, they primarily optimize the use of existing resources rather than fundamentally reducing the model's size and complexity. Thus, model distillation combined with quantization offers the best cost-per-quality tradeoff, making them the most effective strategies for scaling LLMs efficiently in production (Maruti Tech, 2023). 

**Citations:**
- Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network.
- Jacob, B., Kligys, S., Chen, B., et al. (2018). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.
- Maruti Tech. (2023). How to Reduce LLM Costs: Top 6 Cost Optimization Strategies.