Failing at Using a Local LLM for Vinyl Record Color Extraction (tylergaw.com)

A developer recently attempted to transition a vinyl record color extraction project from the OpenAI API to a local large language model (LLM) running on Ollama, but encountered significant performance issues. The project, which utilizes structured data extraction from raw vinyl descriptions, performed well with OpenAI's GPT-4o but struggled with local models due to slow processing speeds. In testing various models, the llama3.1-8b model offered the best quality results but was approximately 336 times slower than GPT-4o, taking around 56 minutes for tasks that required only 10 seconds with OpenAI's offering. The significance of this experiment lies in the exploration of local LLMs, as many developers are increasingly interested in self-contained systems that avoid reliance on external APIs. Despite the challenges faced, including a catastrophic failure when testing a larger model exceeding the machine's RAM capacity, the developer noted that quality outputs from local models like llama3.1-8b showed promise for future projects, although the processing speed may hinder practical applications. This highlights ongoing concerns in the AI/ML community regarding the efficiency and accessibility of localized AI solutions versus cloud-based models.