📝 Guest Post: Meet LoRAX: The Open Source System that Serves 1000s of Fine-Tuned LLMs on a Single GPU*

In this guest post, Travis Addair, CTO and Co-founder of Predibase, introduces LoRAX, their open-sourced solution to the challenges of serving fine-tuned LLMs. He provides an in-depth exploration of LoRAX's inner workings and explains how you can begin utilizing LoRAX in your projects. Building with large language models (LLMs) is at the top of every developer’s to-do list and teams that have progressed beyond simple experimentation are quickly realizing that smaller open-source LLMs like LLaMA-2 outperform costly general-purpose commercial models like GPT-4 when fine-tuned for a specific task. But even though these fine-tuned models are relatively small compared to GPT-4, existing LLM inference systems require each model to be hosted on its own dedicated GPU hardware. This can quickly add up to tens of thousands of dollars per month in cloud costs for just a handful of fine-tuned models. In contrast, one of the most popular commercial LLM APIs – OpenAI’s gpt-3.5-turbo – charges just $

In this guest post, Travis Addair, CTO and Co-founder of Predibase, introduces LoRAX, their open-sourced solution to the challenges of serving fine-tuned LLMs. He provides an in-depth exploration of LoRAX's inner workings and explains how you can begin utilizing LoRAX in your projects. Building with large language models (LLMs) is at the top of every developer’s to-do list and teams that have progressed beyond simple experimentation are quickly realizing that smaller open-source LLMs like LLaMA-2 outperform costly general-purpose commercial models like GPT-4 when fine-tuned for a specific task