Recent advancements in large language models (LLMs) have focused on reducing inference costs and improving performance. Techniques to fine-tune LLMs, traditionally requiring significant computational power, have been transformed by innovative methods. Hyperbolic Labs claims up to 75% compute cost savings for AI applications, benefiting GPU suppliers with idle machines. Stanford researchers introduced Archon, a framework that reduces inference costs and enhances LLM performance. Latent AI's LEIP also aims to cut cloud AI costs without sacrificing performance. Ten effective strategies have been identified to lower LLM inference costs.