Runpod Blog.

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

Learn how to deploy Meta’s powerful Llama 3.1 405B model on Runpod using Ollama, and interact with it through a web-based chat UI in just a few steps.

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.

Learn how to optimize your serverless GPU deployment on Runpod to balance latency, performance, and cost. From active and flex workers to Flashboot and scaling strategy, this guide helps you build an efficient AI backend that won’t break the bank.

Learn when to use open source vs. closed source LLMs, and how to deploy models like Llama-7B with vLLM on Runpod Serverless for high-throughput, cost-efficient inference.

Runpod has reduced prices by up to 40% across Serverless and Secure Cloud GPUs—making high-performance AI compute more accessible for developers, startups, and enterprise teams.

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.