A Review of Large Language Model Optimization and Performance Evaluation

In closed systems, large language models (LLMs) have been shown to perform incredibly well in natural language understanding and text generation. Due to computing overhead, memory, and energy usage, however, deploying these models in real-world applications remains a significant challenge. Large-scale deployment has been hindered by issues ranging from overfitting due to limited diversity in training data, inference inefficiency, and difficulty in maintaining performance consistency across tasks. Making LLMs more practical for widespread use also involves finding a balance between model accuracy, efficiency, and scalability. This paper considers significant advances in LLM optimization, such as quantization techniques such as FP4 and 1-bit LLMs, memory-saving training techniques such as ZeRO, and real-time inference optimizations such as StreamingLLM. It also explores fine-tuning methods that increase model versatility at the expense of reduced computing expenditure, such as QLoRA and LoRA. The research also discusses significant datasets used for LLM training and performance evaluation, highlighting how they ensure model resilience. This paper tries to provide insights on developing scalable, efficient, and cost-effective LLMs that can meet real-world requirements while reducing resource limitations by determining research gaps and reviewing new optimization methods.