On the AI Engineer podcast, Daniel Han of Unsloth discussed compressing Zhipu AI's GLM 5.2, a trillion-plus-parameter model that ships at roughly 1.5 terabytes in BF16 precision, down to about 250 gigabytes. Han stated that randomly compressing parts of the model "will not be 86% dumber" but "will be 100% dumber," emphasizing that successful compression depends on knowing which parts can be squeezed. The panel, assembled by NVIDIA's Chris Alexiuk, included Han, Build from NVIDIA's Model Optimizer team, Marv from Hugging Face, and Parth from Ollama. Asked to define compression, the panel converged on "democratization." Parth framed it as the engine of running local models on consumer hardware. Marv agreed that compression "democratizes the models for everyone at edge devices, at your computer" but warned there is "absolutely zero free lunch." Build reduced it to an efficiency equation: "same cost, more intelligence," noting that the migration from FP32 training to FP4 inference represents an 8x compression at "almost the same intelligence." Chris Alexiuk described the dynamic as an arms race: "It's an ongoing arms race between how big can we make the models versus how small can we make them and they still work."