Devin works with every AI model, so I ditched my expensive cloud setup for a local LLM
The shift to a local LLM reduces reliance on cloud services. Users can now run models like Qwen 3.5-9B on their own hardware. This change affects cost and performance expectations.
Devin has become a preferred choice for users seeking a robust local AI model harness. It supports a wide range of models, including Qwen 3.5-9B and Qwen3-coder-30B-A3B, offering flexibility that other tools like OpenCode and Aider lack. The ability to run models locally has made it an attractive alternative to cloud-based solutions, especially for those looking to avoid ongoing subscription costs.
The transition to local models has been driven by the need for greater control and customization. Tools like Devin Desktop and Devin User Settings provide features that are essential for developers and power users. This shift reflects a growing trend toward self-hosted AI solutions, which can be more cost-effective in the long run.
The use of Qwen 3.5-9B is a common starting point for many users, but those with more powerful hardware can upgrade to models like Qwen3-coder-30B-A3B. This scalability is a key advantage of using a local setup, as it allows users to tailor their AI experience to their specific needs and resources.
The move to local AI models can lead to significant cost savings, but it also requires a higher initial investment in hardware. Users must consider factors like RAM and GPU capabilities when setting up their local environment. Additionally, managing local models may introduce complexities related to updates, maintenance, and security, which are typically handled by cloud providers.
Despite the challenges, the shift to local models is gaining traction. Users are increasingly prioritizing control, privacy, and cost efficiency, even if it means dealing with the technical demands of self-hosted AI. This trend is likely to influence the broader market, pushing more tools and platforms to support local deployment options.