Users frustrated that giant "open weight" models can't actually run locally

A Reddit post on r/LocalLLaMA expresses fatigue with recent massive "open weight" model releases like GLM-5.2. GLM-5.2 has 753 billion parameters, a 1 million token context window, and an MIT license, with benchmark scores that look extremely strong. The problem is that a MoE (mixture-of-experts) model of 700B+ parameters simply cannot be loaded on home hardware.

The poster describes running a dual-GPU setup on an x8/x8 bifurcation board, heavily optimized under AMD ROCm, yet even crushing the model down to q1 or q2 quantization still makes it physically impossible to load. They recall when the community used to focus on genuine self-hosting: compile tricks, tuning llama.cpp batch sizes, testing new quants, and actually fitting models onto personal hardware. Now, they argue, roughly half the posts are effectively free marketing for models that 99% of users can only access by paying for APIs or renting cloud instances.

They acknowledge it's good that the weights are published rather than locked away, but argue that for ordinary people without enterprise-grade hardware, these models are practically no different from closed-source ones.