Stability.ai’s SD3.5 is here—with new models built for speed and quality. Learn what’s changed, what’s improved, and how to run it on Runpod.

NVidia’s Llama 3.1 Nemotron 70B is outperforming larger and closed models on key reasoning tasks. In this post, Brendan tests it against a long-unsolved challenge: consistent, in-character roleplay with zero internal monologue or user coercion—and finds it finally up to the task.

Nemotron 70B is NVIDIA’s latest open model and it’s climbing the leaderboards. But how does it perform in the real world—and can it solve your toughest inference challenges?

Skip the front ends—learn how to use Jupyter Notebook on RunPod to run Stable Diffusion directly in Python. Great for devs who want full control.

Large language models can write poetry and solve logic puzzles—but fail at tasks like counting letters or doing math. Here’s why, and what it tells us about their design.

Lower VRAM usage and improve inference speed using GGUF quantized models in KoboldCPP with just a few environment variables.

GGUF quantizations make large language models faster and more efficient. This guide walks you through using KoboldCPP to load, run, and manage quantized LLMs on Runpod.