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.

Runpod introduces new Serverless pricing with Flex and Active worker types, offering better scalability and up to 40% lower costs for consistent workloads.

Runpod’s new FlashBoot technology slashes cold-start times for serverless GPU endpoints, delivering speeds as low as 500ms. Available now at no extra cost, FlashBoot dynamically optimizes deployment for high-volume workloads—cutting costs and improving latency dramatically.

If you're using the Automatic1111 Stable Diffusion repo as an API layer, startup speed matters. This post explains two key Docker-level optimizations—caching Hugging Face files and precomputing model hashes—to reduce cold start time in serverless environments.

Learn how to use Runpod’s DreamBooth API to automate training and image generation. This guide covers preparing training data, sending requests via Postman, checking job status, and retrieving outputs, with tips for customizing models and prompts.

This guide explains how to launch a Runpod instance using the "Runpod Fast Stable Diffusion" template and train Dreambooth models using the included Jupyter notebooks. The post walks users through deploying the pod, connecting to JupyterLab, preparing instance images, setting training parameters, and running the Dreambooth training workflow. It also covers optional steps such as captioning, adding concept images, testing the trained model using Automatic1111, and uploading to Hugging Face.

This tutorial walks you through deploying Oobabooga’s Text Generation WebUI using the RunPod template. Includes steps for loading Pygmalion 6B and customizing your chatbot.