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Machine learning isn’t just for engineers. In the latest post from our?Learn AI With Me: No Code?series, we break down machine learning in a way that’s clear, approachable—and genuinely useful for anyone curious about AI. ???What?is?machine learning, really? ???What do models learn from, and how do they improve? ???What’s the difference between training, fine-tuning, and inference? ???And why does it all require so much compute? Whether you're a marketer, designer, founder, or simply AI-curious, this post is a great starting point for understanding how modern AI works—and how to start experimenting with it. ??Read Part 2:?Machine Learning Basics for People Who Don’t Code This series is part of our commitment to making AI infrastructure more accessible to everyone—not just developers.

We've launched a data center in Japan! 日本にデータセンターを開設しました！ Users can expect better performance across the Asia-Pacific region

$100 free GPU credits for ARC-AGI-2 participants! This open challenge, offering a $1M prize, pushes the boundaries of artificial general intelligence by focusing on true generalization rather than just pattern matching. At RunPod, we believe that access to compute should never limit innovation. Our platform allows you to spin up high-performance GPUs instantly, run complex models, and collaborate openly. Join the movement towards more transparent and accessible AI research. Apply for your credits below!

Open-Source AI Models Are Leveling Up – Here’s What’s New Remember when AI-generated video was a novelty, and running a state-of-the-art LLM required enterprise-scale compute? That’s changing fast. From Mochi 1’s buttery-smooth motion to SkyReels V1’s cinematic realism, open-source video generation is closing the gap with proprietary models. Meanwhile, new LLMs like QwQ-32B and Gemma 3 are proving that "bigger isn't always better", delivering top-tier performance on increasingly modest hardware. We just rounded up the most exciting open-source video models & LLMs making waves right now—and how you can run them on RunPod today. ?? Check out the full breakdown in the comments.?? #AI #MachineLearning #OpenSource #LLMs #VideoGeneration

Are you code-curious? AI is ready for you. AI’s next wave isn’t just engineers building models—it’s non-coders using AI to build AI tools. No-code platforms and cloud GPUs are making AI more accessible than ever. In our new blog series, “Learn AI With Me,” we break down AI, ML, and LLMs without the jargon. Follow along as we experiment with AI hands-on. ?? in comments #AI #MachineLearning #CodeCurious #NoCodeAI #MLEngineering #CloudComputing #LearnAIWithMe

Announcing: RunPod API We built a REST API so you can control RunPod with code instead of clicks. Now you can: - Script pod creation and termination - Integrate RunPod into your backend - Manage your GPU fleet programmatically The full API lets you upload files, run commands, monitor status, and terminate pods. You can also manage endpoints, templates, and network volumes. Link to docs: https://lnkd.in/g_CaBckU

RunPod is now SOC 2 Type 1 certified ?? Our security controls work as described, your data is protected, and our systems are built to stay available. For teams running business-critical AI workloads, this means one less thing to worry about. Read more: https://lnkd.in/gHt3ri6Y

Exciting news from the RunPod team! ?? We’re growing, and with that growth comes new voices shaping our story. Our new Content Marketing Manager, Alyssa, just shared why she took the leap into AI infrastructure and high-performance compute—and why RunPod was the right place for her. Her blog dives into courage over comfort, AI storytelling, and the future of cloud GPUs. Check it out: ?? https://lnkd.in/gKKKsfJf #AI #CloudComputing #Marketing #ContentMarketing #RunPod #CareerGrowth #MachineLearning

?? Big things are coming at RunPod! ?? We’re thrilled to see Murat Magomedov featured in Built In’s latest article, where he shares his excitement for MultiNode—RunPod’s upcoming distributed GPU cluster technology. With the rise of massive AI models, scaling compute efficiently has never been more critical. Murat and his team have been pioneering a hardware-accelerated isolation engine, allowing GPUs to work together seamlessly in a high-bandwidth, software-defined overlay network. The result? AI developers can train and fine-tune large models at scale while maintaining strict isolation and security in the public cloud. Murat puts it best: “Seeing beta customers try out this feature on large, highly networked Nvidia H100 clusters and run their demanding workloads at scale is nothing short of thrilling.” Scaling AI infrastructure is no small feat, but at RunPod, we’re building for the future—where high-performance compute meets accessibility and efficiency. ?? What’s your biggest challenge in scaling AI workloads today? Let’s talk. ?? Read it here: https://lnkd.in/gKfjNTCd #AI #CloudComputing #GPUs #MachineLearning #RunPod #HighPerformanceComputing

Announcing: Network Volumes for CPU Pods + CPU serverless workers You can now attach network volumes to CPU pods and CPU serverless workers on RunPod. Network volumes persist even after pods shut down and can be mounted across many pods. This makes them ideal for: - Downloading large models - Preprocessing datasets - Doing general-purpose tasks before using GPUs For example, you can now download and prepare models or datasets using lower-cost CPU pods, then mount that same network volume onto your GPU pods when you're ready to run jobs. Available now in your RunPod console.