Scaling scientific world models requires co-designing architectures, training objectives, and numerics. Today, we share the first posts in our series on low-precision pretraining, starting with NVIDIA's NVFP4 recipe for stable 4-bit training. Part 1: Part 2: We cover floating point fundamentals, heuristics, custom CUDA kernels, and stabilization techniques. Future entries will cover custom recipes and results on hybrid architectures.