In this round, I want to emphasize the advantages of prompt caching and request batching as effective strategies to reduce LLM inference costs without degrading model quality. These methods optimize the use of existing resources, providing substantial cost savings over time. 

Prompt caching allows frequently used inputs to be stored and reused, minimizing redundant computations and drastically reducing latency. According to Clarifai (2023), semantic caching can lead to significant performance improvements by avoiding repeated processing of identical requests. This might not reduce model size, but it maximizes efficiency and cost-effectiveness in production environments.

Request batching further enhances throughput by processing multiple requests simultaneously, significantly lowering the per-request cost. Batching can lead to up to a 30% reduction in costs, as it allows for more efficient use of hardware resources (NVIDIA, 2023).

While I acknowledge Alice's points on model distillation and quantization, these approaches often require extensive retraining and may not be immediately feasible for all applications. Carol's emphasis on routing strategies is valuable, but I argue that prompt caching and batching can be implemented quickly and yield immediate cost benefits. 

**Sources:**
- Clarifai Guide. "LLM Inference Optimization Techniques."
- NVIDIA Developer. "Mastering LLM Techniques: Inference Optimization."