Local Inference with Large Language Models (jacquesmattheij.com)

A recent article delves into the increasing significance of local inference with Large Language Models (LLMs), highlighting the author's decision to run models locally to maintain confidentiality for high-stakes client reports. As companies become more reliant on AI tools, concerns over data privacy and reliance on external AI service providers have led some, like the author, to favor local setups. This choice mitigates risks associated with sharing sensitive information and enhances control over their workflow, reflecting a growing trend in the AI/ML community toward local deployment. The article outlines essential components for effective local inference, including powerful hardware, suitable models, and reliable inference engines. Key options discussed include Llama.cpp, favored for its ease of use and stability, and vLLM, which provides higher performance but requires specialized hardware. Models like the Qwen family and DeepSeek are highlighted for their versatility and performance. This shift toward local inference not only addresses confidentiality concerns but also offers researchers and businesses greater autonomy over their AI applications, underscoring an evolution in how LLMs are utilized in sensitive environments.