Runpod Blog.

Learn how to train, deploy, and scale AI/ML models using Runpod Serverless. This guide covers real-world examples, deployment best practices, and how serverless is unlocking new possibilities like real-time video generation.

Learn how to deploy OpenAI’s Faster Whisper on Runpod Serverless to transcribe and translate audio up to four times faster and at a fraction of the cost of Whisper, using Python for efficient, scalable speech-to-text automation.

Learn how to fine-tune Large Language Models (LLMs) using Runpod and Axolotl to deploy GPU pods, train a Llama 3 model with LoRA, and test customized models efficiently without starting from scratch.

An AWS us-east-1 outage degraded Runpod’s control plane, but Pods kept running with no data loss, and within 72 hours we added multi-region failover, cached Serverless configs, corrected charges, and started a partitioned multi-region migration on Runpod’s provider network.

Runpod has officially achieved SOC 2 Type II certification, validating that its enterprise-grade security controls not only meet strict design standards but also operate effectively over time. This milestone proves Runpod’s ongoing commitment to protecting customer data and maintaining trusted, compliant AI infrastructure for enterprises and developers alike.

Learn how to deploy ComfyUI as a serverless API endpoint on Runpod to run AI image generation workflows at scale. The tutorial covers deploying from Runpod Hub templates or Docker images, integrating with Python for synchronous API calls, and customizing models such as FLUX.1-dev or Stable Diffusion 3. Runpod’s pay-as-you-go Serverless platform provides a simple, cost-efficient way to build, test, and scale ComfyUI for generative AI applications.

Slurm on RunPod Instant Clusters makes it simple to scale distributed AI and scientific computing across multiple GPU nodes. With pre-configured setup, advanced job scheduling, and built-in monitoring, users can efficiently manage training, batch processing, and HPC workloads while testing connectivity, CUDA availability, and multi-node PyTorch performance.