AI Development

The Future of AI Training: Are GPUs Enough for the Next Generation of AI?

AI workloads are evolving fast. GPUs still dominate training in 2025, but emerging hardware and hybrid infrastructure are reshaping the future. Here’s what GTC 2025 reveals—and how RunPod fits in.

Llama-4 Scout and Maverick Are Here—How Do They Shape Up?

Meta has been one of the kings of open source, open weight large language models. Their first foray with Llama-1 in 2023, while limited in its application and licensing, was a clear direction to the community that there was an alternative to large closed-off models. Later in 2023 we got

Built on RunPod: How Cogito Trained High-Performance Open Models on the Path to ASI

At RunPod, we're proud to power the next generation of AI breakthroughs—and this one is big. San Francisco-based Deep Cogito has just released Cogito v1, a family of open-source models ranging from 3B to 70B parameters. Each model outperforms leading alternatives from LLaMA, DeepSeek, and Qwen in

AI Development

How AI Helped Win a Nobel Prize - Protein Folding and AI

AlphaFold just won the Nobel Prize—and proved AI can solve problems once thought impossible. This post explores what it means for science, compute, and how RunPod is helping make the next breakthrough accessible to everyone.

No-Code AI

No-Code AI: How I Ran My First Language Model Without Coding

I wanted to run an open-source AI model myself—no code, just curiosity. Here’s how I deployed Mistral 7B on a cloud GPU and what I learned.

Bare Metal

Bare Metal vs. Instant Clusters: Which Is Right for Your AI Workload?

Instant Clusters are here. RunPod’s newest deployment option lets you spin up multi-node environments in minutes—no contracts, no config files. Learn how they compare to Bare Metal and when to use each for your AI workloads.

Introducing Instant Clusters: Multi-Node AI Compute, On Demand

Until now, RunPod users could generally scale up to 8 GPUs in a single pod. For most use cases—like running inference on Llama 70B or fine-tuning FLUX—that was plenty. But some workloads need more compute than a single server. They need to scale across multiple machines. Today, we’

No-Code AI

Machine Learning Basics for People Who Don't Code

You don’t need to know code to understand machine learning. This post breaks down how AI models learn—and how you can start exploring them without a technical background.

RunPod Expands in Asia-Pacific with Launch of AP-JP-1 in Fukushima

We're excited to announce the launch of AP-JP-1, RunPod's first data center in Japan—now live in Fukushima. This marks a major step forward in our global infrastructure strategy and opens the door to dramatically better performance for users across the Asia-Pacific region. Why This Matters

Supporting the Future of AGI: RunPod Partners with ARC Prize 2025

The race toward artificial general intelligence isn't just happening behind closed doors at trillion-dollar tech companies. It's also unfolding in the open—in research labs, Discord servers, GitHub repos, and competitions like the ARC Prize. This year, the ARC Prize Foundation is back with ARC-AGI-2, a

Introducing Easy LLM Fine-Tuning on RunPod: Axolotl Made Simple

At RunPod, we're constantly looking for ways to make AI development more accessible. Today, we're excited to announce our newest feature: a pre-configured Axolotl environment for LLM fine-tuning that dramatically simplifies the process of customizing models to your specific needs. Why Fine-Tuning Matters Fine-tuning large language

Introducing Bare Metal: Dedicated GPU Servers with Maximum Control and Savings

AI teams and ML engineers need flexibility, performance, and cost efficiency—and RunPod's new Bare Metal offering delivers exactly that. With Bare Metal, you can now reserve dedicated GPU servers for months or even years, ensuring consistent performance without the hassle of hourly or daily pricing. This means

Deploying Multimodal Models on RunPod

Multimodal AI models integrate various types of data, such as text, images, audio, or video, to allow tasks such as image-text retrieval, video question answering, or speech-to-text. Examples are CLIP, BLIP, and Flamingo, among others, showing what is possible by combining these modes–(but deploying them presents unique challenges including

Open Source Video and LLM: New Model Roundup

Remember when generating decent-looking videos with AI seemed like something only the big tech companies could pull off? Those days are officially over. 2024 brought us a wave of seriously impressive open-source video generation models that anyone can download and start playing with. And here's the kicker -

What Even Is AI? (A Writer & Marketer’s Perspective)

Learn AI With Me: The No-Code Series, Part 1 AI Is Everywhere, But What Is It? If you spend any time online, you’ve probably seen the explosion of AI tools—ChatGPT, MidJourney, DALL·E, Claude, and Gemini. Everyone is talking about AI, but when you ask, "What exactly

Training Flux.1 Dev on the MI300X GPU with Huge Batch Sizes

For this post I'm going to be experimenting with fine-tuning Flux.1 Dev on the world's largest GPU, the AMD MI300X. At 192GB of VRAM per GPU, the MI300X allows training at batch sizes and resolutions that no other GPU currently supports. (While these instructions were

Streamline GPU Cloud Management with RunPod's New REST API

Managing GPU resources has always been a bit of a pain point, with most of the time spent clicking around interfaces with repetitive manual configuration. Our new API lets you control everything through code instead, which is great news for those who'd rather automate repetitive tasks and focus

Runpod Platform

RunPod Achieves SOC 2 Type I Certification: A Major Milestone in Security and Compliance

RunPod has successfully completed its SOC 2 Type I audit, conducted by Sensiba. This marks a significant milestone in our commitment to security, compliance, and trust for our customers and partners.

AccessibleTech

AI, Content, and Courage Over Comfort: Why I Joined RunPod

Alyssa Mazzina Every big move in my career has come down to one thing: choosing courage over comfort. That's why, when I discovered RunPod’s bold, developer-first approach to AI infrastructure, even though I knew next to nothing about AI infrastructure, I knew this was exactly where I

Cloud Computing

Unveiling Enhanced CPU Pods: Docker Runtime and Network Volume Support

We're excited to announce two significant upgrades to our CPU pods that will streamline your development workflow and expand storage options. Our CPU pods now feature Docker runtime (replacing Kata Containers) and support for network volumes—previously exclusive to our GPU pods.

Run Deepseek R1 On Just 480GB of VRAM

Even with the new closed model successes of Grok and Sonnet 3.7, DeepSeek R1 is still considered a heavyweight in the LLM arena as a whole, and remains the uncontested open-source LLM champion (at least until DeepSeek R2 launches, anyway.) We've written before about the concerns of

GitHub Integration Now In GA - Build Images from GitHub Repos Even Faster

RunPod is pleased to announce that our GitHub integration is officially out of beta and ready for production use! This feature enables you to iterate your work more quickly by building packages to deploy on RunPod serverless directly from a GitHub repo, removing all of the friction involved in creating

AI Development

How Online GPUs for Deep Learning Can Supercharge Your AI Models

Online GPUs provide on-demand access to high-performance cloud computing, allowing AI teams to scale resources instantly without heavy infrastructure costs.

AI Development

How to Choose a Cloud GPU for Deep Learning: The Ultimate Guide

Cloud GPUs allow organizations to dynamically scale resources, optimize workflows, and tackle the most demanding AI tasks while effectively managing costs. This guide delves into the benefits of cloud GPUs for deep learning and explores key factors to consider when choosing a provider.

Introduction to Websocket Streaming with RunPod Serverless

In this followup to our 'Hello World' tutorial, we'll create a serverless endpoint that processes base64-encoded files and streams back the results. This will demonstrate how you can work with file input/output over our serverless environment by encoding the file as data within a JSON