Running local models is good now (vickiboykis.com)

Recent advancements in local AI models have significantly improved their usability and performance, particularly for coding tasks. A tech enthusiast tested several local models, including Mistral 7B and Google’s latest Gemma 4 series, on a powerful 2022 M2 Mac, highlighting a major shift in local model capabilities. The individual noted that early local models were slow, inaccurate, and difficult to use, but with models like GPT-OSS and the latest Gemma 4, they are now capable of coding tasks with approximately 75% efficiency compared to leading API models. This marks a pivotal moment, showcasing that local models can now serve as an efficient alternative to online solutions for development queries. This development is significant for the AI and machine learning community as it opens up new avenues for personalization and control, enabling users to run powerful models without relying on cloud-based solutions. The improved architecture of newer models raises questions about performance trade-offs in a landscape increasingly dominated by the quest for larger token capabilities. Despite some limitations—like slower inference times and restricted context windows—the growing ecosystem of local models, combined with tools like Docker and LM Studio, offers exciting opportunities for developers to experiment with and optimize their AI applications, enhancing the landscape of local AI model deployment.