**Consensus Verdict on Strategies for Reducing LLM Inference Costs**

**1. Areas of Agreement:**
All debaters recognize the importance of optimizing inference costs for large language models (LLMs) and agree that multiple strategies can be effective. They acknowledge that both model-level optimizations (distillation and quantization) and operational strategies (prompt caching, batching, and routing) can lead to significant cost reductions.

**2. Areas of Disagreement:**
The primary disagreement lies in the emphasis on model-level optimizations versus operational strategies. Alice advocates for distillation and quantization as superior long-term solutions, while Bob and Carol prioritize prompt caching and batching for their immediate cost-effectiveness and ease of implementation. Bob and Carol also argue that routing to cheaper models can enhance efficiency without the quality trade-offs associated with model modifications.

**3. Strongest Evidence-Backed Strategies:**
- **Model Distillation and Quantization:** Supported by studies showing substantial performance retention with reduced resource requirements (Sanh et al., 2019; Jacob et al., 2018).
- **Prompt Caching and Batching:** Evidence indicates that these techniques can lead to significant reductions in inference costs and latency, with prompt caching achieving up to 90% cost savings for repeated queries (Wei et al., 2021) and batching increasing throughput by 2-5x (Chen et al., 2020).

**4. Final Recommendation:**
A hybrid approach is recommended, combining the strengths of both operational and model-level strategies. Implementing prompt caching and batching should be prioritized for immediate cost savings and ease of integration. Concurrently, organizations should invest in model distillation and quantization for long-term efficiency and scalability. This dual strategy balances immediate operational benefits with sustainable model performance improvements. **Confidence Level: High.**