By Festus Ewakaa Kahunla The world of Natural Language Processing (NLP) has been buzzing with the advent of Large Language Models (LLMs) that boast billions of parameters. These models, while incredibly powerful, come with their own set of challenges, especially when it comes to deployment and fine-tuning. A recent paper titled "PETALS: Collaborative Inference and Fine-tuning of Large Models" offers a fresh perspective on this issue. Let's dive into the key takeaways from this paper. The Challenge with LLMs Modern LLMs, such as BLOOM-176B, have more than 100 billion parameters. While these models are now available for download, using them requires high-end hardware, which many researchers might not have access to. Techniques like RAM offloading or hosted APIs offer some respite, but they come with their own limitations. For instance, offloading can be slow, and APIs might not offer the flexibility needed for specific research tasks. Introducing PETALS PETALS, a sy