Blog

Runpod Blog

Our team’s insights on building better and scaling smarter.
All
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

The Future of AI Training: Are GPUs Enough?

GPUs still dominate AI training in 2025, but emerging hardware and hybrid infrastructure are reshaping what's possible. Here’s what GTC revealed—and what it means for you.
Read article
AI Workloads

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

Meta’s Llama 4 models, Scout and Maverick, are the next evolution in open LLMs. This post explores their strengths, performance, and deployment on Runpod.
Read article
Hardware & Trends

Built on RunPod: How Cogito Trained Models Toward ASI

San Francisco-based Deep Cogito used RunPod infrastructure to train Cogito v1, a high-performance open model family aiming at artificial superintelligence. Here’s how they did it.
Read article
AI Workloads

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

Curious but not technical? Here’s how I ran Mistral 7B on a cloud GPU using only no-code tools—plus what I learned as a complete beginner.
Read article
Learn AI

Bare Metal vs. Instant Clusters: What’s Best for Your AI Workload?

Runpod now offers Instant Clusters alongside Bare Metal. This post compares the two deployment options and explains when to choose one over the other for your compute needs.
Read article
AI Infrastructure

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

Runpod’s Instant Clusters let you spin up multi-node GPU environments instantly—ideal for scaling LLM training or distributed inference workloads without config files or contracts.
Read article
AI Infrastructure

Machine Learning Basics (for People Who Don’t Code)

You don’t need to code to understand machine learning. This guide explains how AI models learn, and how to explore them without a technical background.
Read article
Learn AI

Build what’s next.

The most cost-effective platform for building, training, and scaling machine learning models—ready when you are.