Accelerating Local AI Chatbot Processing Speed: Model Evaluation and Advanced Algorithmic Enhancements Using Machine Learning

Abstract

Local AI chatbots regularly suffer from high latency and low processing speed, which hinder real-time user interaction and reduce operational effectiveness. This study focuses on accelerating the speed and enhancing the performance of local chatbots by applying advanced algorithmic techniques such as model pruning, random forests, reinforcement learning, and neural architecture search (NAS). A quantitative approach is followed using the "Local Chatbot Performance Metrics" dataset, which includes performance data from 1,000 locally developed AI chatbots. This dataset provides a comprehensive evaluation of chatbot speed, accuracy, and computational efficiency across multiple domains. For real-world applications, performance is assessed using metrics such as latency, resource utilization, and response accuracy. In controlled tests, enhanced models are compared to their baseline models to produce findings that may be applicable in practical contexts. To improve chatbot efficiency, the study also utilizes Knowledge Distillation, Quantization, and LoRA. While this study provides important contributions, subsequent studies incorporating qualitative analysis to obtain user feedback and evaluate the effects of new technology on chatbot performance as a whole, are needed.

Keywords: AI Chatbots, Processing Speed, Machine Learning, Model Pruning, User Experience