This technical report evaluates the effectiveness of Low Rank Adaptation (LoRA) for fine-tuning Large Language Models (LLMs) and demonstrates the practicality of deploying multiple LoRA fine-tuned models in real-world applications. The study assesses 310 LLMs fine-tuned with LoRA across 10 base models and 31 tasks, finding that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. The results show that LoRA fine-tuned models can rival GPT-4 in performance, with Mistral-7B and Zephyr-7B models performing best after fine-tuning. The study also investigates the effectiveness of different base models for fine-tuning and the impact of task complexity on fine-tuning outcomes. It evaluates the latency and concurrency capabilities of LoRAX, an open-source inference server that allows multiple LoRA fine-tuned models to be deployed on a single GPU. The study concludes that LoRA fine-tuning is a cost-effective and efficient method for improving LLM performance, and that deploying multiple specialized LLMs can be more effective than a single general-purpose LLM. The findings highlight the potential of LoRA for parameter-efficient fine-tuning and the practical benefits of deploying multiple LoRA models in production environments.This technical report evaluates the effectiveness of Low Rank Adaptation (LoRA) for fine-tuning Large Language Models (LLMs) and demonstrates the practicality of deploying multiple LoRA fine-tuned models in real-world applications. The study assesses 310 LLMs fine-tuned with LoRA across 10 base models and 31 tasks, finding that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. The results show that LoRA fine-tuned models can rival GPT-4 in performance, with Mistral-7B and Zephyr-7B models performing best after fine-tuning. The study also investigates the effectiveness of different base models for fine-tuning and the impact of task complexity on fine-tuning outcomes. It evaluates the latency and concurrency capabilities of LoRAX, an open-source inference server that allows multiple LoRA fine-tuned models to be deployed on a single GPU. The study concludes that LoRA fine-tuning is a cost-effective and efficient method for improving LLM performance, and that deploying multiple specialized LLMs can be more effective than a single general-purpose LLM. The findings highlight the potential of LoRA for parameter-efficient fine-tuning and the practical benefits of deploying multiple LoRA models in production environments.