📝 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…

related reading

• LoRA Without Regret - Thinking Machines Labthinkingmachines.ai
• [2106.09685] LoRA: Low-Rank Adaptation of Large Language Modelsarxiv.org
• Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA) - Lightning AIlightning.ai
• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
• ⭐️ Fast LLM Inference From Scratchandrewkchan.dev
• Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem – PyTorchpytorch.org
• Anatomy of a Modern Finetuning APIbenanderson.work
• Look Ma, No Bubbles! Designing a Low-Latency Megakernel for Llama-1B · Hazy Researchhazyresearch.stanford.edu
• Transformers Inference Optimization Toolset | AstraBlogastralord.github.io
• In-depth guide to fine-tuning LLMs with LoRA and QLoRA | Mercity Researchmercity.ai
• How is LLaMa.cpp possible?finbarr.ca
• Efficient LLM Finetuning with Unsloth | Modal Docsmodal.com