Runpod Blog.

Finished training your model in a Pod? This guide helps you decide when to switch to Serverless, what trade-offs to expect, and how to optimize for fast, cost-efficient inference.

No GPU. No team. Just $5. This is how one solo developer used Runpod Serverless to build and deploy a working AI product—"AI for Dads"—without writing any custom training code.

RunPod now integrates directly with AI IDEs like Cursor and Claude Desktop using MCP. Launch pods, deploy endpoints, and manage infrastructure—right from your editor.

Alibaba’s Qwen3 is here—with major performance improvements and a full range of models from 0.5B to 72B parameters. This post breaks down what’s new, how it compares to other open models, and what it means for developers.

Different stages of AI development call for different infrastructure. This post breaks down when GPU clusters shine—and how to scale up only when it counts.

Discover how Krnl transitioned from AWS to Runpod’s Serverless GPUs to support millions of users—slashing idle cost and scaling more efficiently.

MoE models scale efficiently by activating only a subset of parameters. Learn how this architecture works, why it’s gaining traction, and how Runpod supports MoE training and inference.