Local LLM Inference: Potential is Huge, But Tooling Needs to Mature

This article benchmarks the performance of local LLM inference frameworks such as llama.cpp, Ollama, and WebLLM. Results show llama.cpp and Ollama are blazing fast, but still slower than OpenAI's gpt-4.0-mini. A bigger challenge lies in model selection and deployment: the sheer number of model versions is overwhelming, and even a quantized 7B model is over 5GB, leading to slow downloads and loading, impacting user experience. The author argues that future local LLM inference needs easier model training and deployment tools, and tight integration with cloud LLMs, to become truly practical.