Optimizing Language Model Speed on Limited VRAM: A Practical Benchmark Study

This benchmark provides essential insights for developers seeking to deploy large language models cost-effectively on consumer-grade GPUs. By testing various models and quantization strategies on 16GB VRAM hardware, the analysis demonstrates that strategic optimization—including proper model selection, quantization levels, and context management—enables significant performance gains, making self-hosted AI accessible without enterprise-level infrastructure investments.