Author name: Runpod Blog.

Join us as we celebrate our fielding of our 10 billionth serverless request.

Large language models like Guanaco 65B can run on Runpod with the right optimizations. Learn how to handle quantization, memory, and GPU sizing.

Runpod’s GitHub integration lets you deploy endpoints directly from a repo—no Dockerfile or manual setup required. Here’s how it works.

See how the NVIDIA RTX 5090 stacks up in large language model benchmarks. We explore real-world performance and whether it’s the top GPU for AI workloads today.

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.

Fine-tuning large language models can require hours or days of runtime. This guide walks through how to choose the right GPU spec for cost and performance.

Confused about spot vs. on-demand GPU instances? This guide breaks down the key differences in availability, pricing, and reliability so you can choose the right option for your AI workloads.

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 Runp…

Compare Google Colab Pro and Runpod across pricing, reliability, and GPU access. Which is the better deal for developers running real AI workloads?

Prefer Google Colab’s interface? This guide shows how to connect Colab notebooks to Runpod GPU instances for more power, speed, and flexibility in your AI workflows.