Open-source large language models (LLMs) are increasingly competing with proprietary models in various natural language processing tasks, offering advanced AI capabilities without the high costs and restrictions associated with closed models. However, deploying these open-source models at scale remains a challenge, which can be addressed through serverless GPU solutions that simplify infrastructure management. The blog post reviews several leading open-source LLMs as of early 2025, such as DeepSeek-R1, Mistral Small 3, and Qwen 2.5 Coder, highlighting their strengths in reasoning, conversational AI, and code generation, respectively. These models, available under open-source licenses, are noted for their competitive performance against proprietary models and their adaptability for specific tasks through fine-tuning. The post also emphasizes the ease of deploying and optimizing these models using platforms like Koyeb, which provide scalable and cost-effective solutions for running AI applications.