Developer solves AI image generation crashes by freeing RAM, not closing editor

A developer running a local FLUX 12B AI image generation pipeline on a 16GB M4 Mac repeatedly hit memory-triggered safety kills that halted output while keeping the machine stable. The core problem was that the model's weights alone consumed around 7–9GB at runtime, leaving no headroom when the dev environment and other apps were also running. After experimenting with lower quantization and isolating variables, the developer found that raising available free memory from 51% to 71% before starting generation — primarily by closing a heavy browser — was the decisive fix. Switching from 4-bit to 3-bit quantization offered an additional ~2.5GB peak reduction, though it proved optional once sufficient RAM was freed. The debugging process highlighted how resident model weights, not output size, drive memory usage in large AI workloads.