Alyssa Mazzina

MoE models scale efficiently by activating only a subset of parameters. Learn how this architecture works, why it’s gaining traction, and how Runpod supports MoE training and inference.

Alyssa Mazzina shares her personal journey to joining RunPod, and why betting on bold, creator-first infrastructure felt like the right kind of risk.

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.

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.

The new NVIDIA RTX 5090 is now live on RunPod. With blazing-fast inference speeds and large memory capacity, it’s ideal for real-time LLM workloads and AI scaling.

Choosing a cloud GPU isn’t just about power—it’s about efficiency, memory, compatibility, and budget. This guide helps you select the right GPU for your deep learning projects.

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.