RAG and fine-tuning are two powerful strategies for adapting large language models (LLMs) to domain-specific tasks. This post compares their use cases, performance, and introduces RAFT—an integrated approach that combines the best of both methods for more accurate and adaptable AI models.

Runpod and Defined.ai launch a pilot program to provide startups with access to high-quality training data and compute, enabling sector-specific fine-tuning and closing the data wealth gap.

Segment Anything Model 2 (SAM 2) offers real-time segmentation power. This guide walks you through running it efficiently on RunPod’s cloud GPUs.

Discover how to deploy Meta's Llama 3.1 using RunPod’s new vLLM worker. This guide walks you through model setup, performance benefits, and step-by-step deployment.

Retrieval-Augmented Generation (RAG) and fine-tuning are powerful ways to adapt large language models. Learn the key differences, trade-offs, and when to use each.

Step-by-step guide for deploying FLUX with ComfyUI on Runpod. Perfect for creators looking to generate high-quality AI images with ease.

Learn how to deploy Meta’s powerful open-source Llama 3.1 405B model using Ollama on RunPod. With benchmark-crushing performance, this guide walks you through setup and deployment.