Why Your Local LLM Feels Dumber Than It Is: Quantization, Context, and Sampling Explained
If your local LLM keeps producing worse answers than the cloud model you’re used to, the cause is almost never “my model is dumb.” In most cases the model is fine, but three hidden variables — quantization, context management, and sampling parameters — are quietly degrading its output. Each one is measurable, understood, and fixable. Quantization is usually the least likely culprit; a lost-in-the-middle context problem or a badly tuned sampler is far more often the real reason a local model feels weak. Diagnose those first, and you’ll often unlock the quality you thought you lost. ...