In the ongoing discussion of strategies to reduce LLM inference costs, I would like to emphasize the effectiveness of prompt caching and routing to cheaper models as the easiest to implement. Prompt caching allows for the reuse of previously generated responses, leading to significant cost savings. According to a study by CalmOps (2023), prompt caching can reduce inference costs by over 70%. This method optimizes resource utilization without compromising response quality, making it a highly efficient strategy.

Additionally, routing to cheaper models enables adaptive model selection based on the complexity of the task at hand. By employing a tiered model approach, organizations can dynamically choose less expensive models for simpler requests, thereby optimizing costs while maintaining acceptable performance levels. This strategy aligns with the findings from Aiveda (2023), which indicate that smaller models can produce faster responses, allowing businesses to handle higher request volumes with fewer resources.

While I acknowledge Alice's point on model distillation and quantization, these methods often require more upfront investment in training and may lead to performance trade-offs that are not necessary when simpler strategies like caching and adaptive routing can achieve substantial cost reductions with minimal implementation complexity.

**Citations:**
- CalmOps. (2023). LLM Cost Optimization: Reducing Inference Costs 70%+.
- Aiveda. (2023). LLM Inference Cost Reduction With Small Language Models.