To optimize a fine-tuned model's performance and minimize inference latency: * Smaller models (like *llama3-8b' with an 8k context window, supporting 6k for prompt and 2k for completion) generally have lower latency for both training and inference. While exceeding the context window results in truncation which can negatively impact quality, for specific tasks, a smaller, fine-tuned model can achieve the required accuracy with better performance. * **B:** Deploying a fine-tuned model to a Snowpark Container Services (SPCS) compute pool with GPU instances (e.g., or is crucial for leveraging GPU acceleration. This is explicitly optimized for intensive GPU usage scenarios like LLMsA/LMs, which significantly reduces inference latency and increases throughput. * It is important to ensure that prompt and completion pairs do not *exceed* the context window to prevent truncation and negative impact on model quality. However, *precisely filling* the context window is not a requirement or an optimization strategy; the focus should be on providing relevant and high-quality data within the model's limits. * '*D:" Setting 'max_epochs' to 1 reduces the *training time*. However, training time does not directly improve *inference* latency for the deployed model. Inference latency depends on the model's architecture, deployment hardware, and runtime optimizations. Furthermore, too few epochs can lead to a poorly performing model, failing the accuracy requirement. * E: This describes using the 'AI CLASSIFY managed function for zero-shot classification, which is an alternative to fine-tuning. While it might avoid the latency associated with fine-tuning *training*, the question is specifically about optimizing the performance of a *fine-tuned model* for a specialized task, implying that fine-tuning is chosen for its potential to achieve higher accuracy for that niche use case compared to zero-shot approaches.