Featured Introducing the Runpod Hub: Where AI Builders Thrive The Runpod Hub is a creator-powered marketplace for open source AI repos. Discover LLMs, image models, and more — then deploy in seconds on Runpod.
AI on Campus: How Students Are Really Using AI to Write, Study, and Think Forget the stereotypes — students aren’t just using AI to cheat. From study guides to schedule planning, here’s how today’s college students are using tools like ChatGPT to learn smarter, stay organized, and support each other along the way.
AI Development Why the Future of AI Belongs to Indie Developers The next wave of AI innovation won’t come from mega-labs — it’ll come from small, fast-moving teams. Learn how indie developers are shaping the future of open-source AI and how platforms like RunPod help them move fast without the infrastructure overhead.
AI Development The Open-Source AI Renaissance: How Community Models Are Shaping the Future The open-source AI movement is rewriting the rules of model development. From community-built tools to powerful fine-tunes, builders are moving faster than ever—with the infrastructure to match. Here’s how open-source AI took off, and where it’s headed next.
InstaHeadshots: Scaling AI-Generated Portraits with RunPod InstaHeadshots is revolutionizing professional photography by transforming casual selfies into studio-quality headshots within minutes. Their AI-driven platform caters to professionals seeking polished images for LinkedIn, resumes, and social media profiles, eliminating the need for traditional photoshoots. The Challenge: Managing Surging Demand and Diverse Workloads As InstaHeadshots experienced rapid growth, they
How Segmind Scaled GenAI Workloads 10x Without Scaling Costs Segmind uses RunPod to dynamically scale GPU infrastructure across its Model API and PixelFlow engine—powering 10x growth with zero idle waste.
Built on RunPod How Glam Labs Powers Viral AI Video Effects with RunPod Glam Labs used RunPod Serverless to train and run viral AI video effects—cutting costs, accelerating development, and scaling content creation with ease.
GPUComputing Why AI Needs GPUs: A No-Code Beginner’s Guide to Compute Power Why AI models need GPUs, how to choose the right one, and what makes cloud GPUs ideal for no-code AI experimentation. A beginner’s guide to compute power.
Built on RunPod Talking to AI, at Human Scale: How Scatterlab Powers 1,000+ RPS with RunPod Learn how Scatterlab scaled to 1,000+ requests per second using RunPod to deliver real-time AI conversations at half the cost of hyperscalers.
From API to Autonomy: Why More Builders Are Self-Hosting Their Models Outgrowing the APIs? Learn when it’s time to switch from API access to running your own AI model. We’ll break down the tools, the stack, and why more builders are going open source.
Built on RunPod How a Solo Dev Built an AI for Dads—No GPU, No Team, Just $5 A solo developer fine-tuned an emotional support AI for dads using Mistral 7B, QLoRA, and RunPod—with no GPU, no team, and under $5 in training costs.
Stable Diffusion How Civitai Scaled to 800K Monthly LoRAs on RunPod Discover how Civitai used RunPod to train over 868,000 LoRA models in one month—fueling a growing creator community and powering millions of AI generations.
From Pods to Serverless: When to Switch and Why It Matters You’ve just finished fine-tuning your model in a pod. Now it’s time to deploy it—and you’re staring at two buttons: Serverless or Pod. Which one’s right for running inference? If you’ve been using Pods to train, test, or experiment on RunPod, Serverless might be
GPU Clusters: Powering High-Performance AI Computing (When You Need It) AI infrastructure isn't one-size-fits-all. Different stages of the AI development lifecycle call for different types of compute—and choosing the right tool for the job can make all the difference in performance, efficiency, and cost. At RunPod, we're building infrastructure that fits the way modern AI
How Krnl Scaled to Millions of Users—and Cut Infra Costs by 65% With RunPod When Krnl’s AI tools went viral, they outgrew AWS fast. Discover how switching to RunPod’s serverless 4090s helped them scale effortlessly, eliminate idle costs, and cut infrastructure spend by 65%.
Mixture of Experts (MoE): A Scalable Architecture for Efficient AI Training Mixture of Experts (MoE) models scale efficiently by activating only a subset of parameters per input. Learn how MoE works, where it shines, and why RunPod is built to support MoE training and inference.
The RTX 5090 Is Here: Serve 65,000+ Tokens per Second on RunPod RunPod customers can now access the NVIDIA RTX 5090—the latest powerful GPU for real-time LLM inference. With impressive throughput and large memory capacity, the 5090 enables serving for small and mid-sized AI models at scale. Whether you’re deploying high-concurrency chatbots, inference APIs, or multi-model backends, this next-gen GPU
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.
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
