Running larger AI models locally produces dramatically better results than smaller ones—even if slower—according to developer tests with Qwen and Unsloth

A developer running Qwen 3.6 (a locally-run AI model) switched from a heavily compressed version (IQ4_XS) to a larger one (IQ4_NL_XL) and found the larger version solved coding tasks with far fewer errors and loops, despite processing tokens—the small chunks of text an AI reads—more slowly.

The key insight: measuring success by raw speed (tokens per second) is misleading. A slower model that gets answers right on the first try completes your actual work faster than a faster model that makes mistakes and needs corrections. In coding tasks, correctness matters more than raw processing speed.

For anyone running AI models at home to avoid cloud costs, this matters: you may be choosing a smaller model just to fit your GPU memory, but if you have even a little headroom, upgrading can cut your total task time and error rate significantly. The better approach is to time how long actual jobs take, not benchmark speed alone.