Local LLMs can seem worse than benchmarks because inference hardware, software, and quantization change model behavior

The article argues that local LLM performance often disappoints because users are not running the same setup as the model’s reference implementation. Differences in GPUs, instruction sets, inference software, and quantization can alter output quality even when the underlying weights are the same.

As more people run models locally, the gap between benchmark claims and real-world performance is becoming a core usability problem. Better inference standards, reproducible test suites, and clearer reporting of hardware and software settings will matter as much as model weights for evaluating LLM quality.