In this tutorial, we explore NVIDIA’s cosmos-framework from a practical Colab-friendly angle while staying honest about the hardware limits of running real Cosmos 3 checkpoints. We begin by checking the current runtime, GPU capabilities, CUDA availability, memory, and disk space to understand why full Cosmos 3 inference is not realistic on standard Colab hardware. Instead of stopping there, we use the framework’s real structure, CLI surface, input schema, and model modes as the foundation for a hands-on miniature implementation. We then build and train a compact omnimodal Mixture-of-Transformers world model that mirrors the core Cosmos idea: shared cross-modal attention with modality-specific expert routing for text, vision, and action streams. Using synthetic physical-world data, training-loss tracking, and an autoregressive rollout, we show how the model learns relationships across modalities and predicts future latent states in a simplified yet technically meaningful way.
Probing Colab Hardware Limits
import os, sys, json, time, math, textwrap, subprocess, shutil, platform
from pathlib import Path
def rule(title=""):
