No GPU, No Problem: Finding the Fastest Small LLM for Budget Hardware

This practical benchmark addresses a critical gap in LLM accessibility by testing real performance on budget hardware without GPU acceleration—an overlooked scenario most benchmarks ignore. By demonstrating that small, efficient language models deliver genuine utility on standard consumer hardware, it democratizes AI development and makes advanced capabilities accessible to students and developers without enterprise resources. The findings validate edge computing as a viable deployment strategy and challenge the assumption that serious AI work requires expensive infrastructure.