Devstral Small 2 on RTX 5060 Ti: Local AI Coding Setup

The setup featuring an RTX 5060 Ti 16GB and 32GB DDR5-6000 RAM, paired with the Devstral Small 2 model, offers impressive local AI coding capabilities without the need for RAM offloading. This configuration excels in maintaining a good token generation speed by fitting everything within the GPU’s VRAM, effectively using the Zed Editor with Zed Agent for efficient code exploration and execution. Despite initial skepticism about handling a dense 24B model, the setup proves capable of generating and refining code, particularly when provided with detailed instructions, and operates at a cool temperature with minimal noise. This matters as it demonstrates the potential for high-performance local AI development without resorting to expensive hardware upgrades.

The combination of the RTX 5060 Ti 16GB GPU with the Devstral Small 2 model offers a powerful setup for local AI coding. This configuration allows for efficient processing without the need for RAM offloading, maintaining a high token generation speed. The ability to fit the 24B model entirely within the GPU’s 16GB VRAM is crucial, as it prevents performance bottlenecks that occur when data is offloaded to slower RAM. This setup is particularly beneficial for developers seeking a local AI solution that can handle complex tasks without compromising speed or efficiency.

Using the Zed editor with Zed Agent enhances the performance of this setup. The shorter system prompt in Zed Agent compared to Claude Code allows for more of the context window to be utilized for the actual project’s code. This is significant because it means more of the model’s capacity can be dedicated to understanding and processing the task at hand, rather than being consumed by lengthy prompts. This advantage is particularly noticeable when working with large codebases or projects that require extensive context to navigate and modify effectively.

The ability of the Devstral Small 2 model to generate code and make necessary modifications autonomously is a testament to its advanced capabilities. Even with initial challenges, such as cloning issues, the model demonstrates adaptability by modifying frameworks to ensure functionality. This is a common trait among advanced language models, which often employ creative problem-solving techniques to achieve desired outcomes. For developers, this means less manual intervention and a more streamlined coding process, which can significantly enhance productivity and reduce development time.

Overall, this setup offers a cost-effective alternative to more expensive hardware or subscription-based AI services. With impressive performance metrics, such as prompt processing speeds of 600-650 tokens per second and token generation speeds of 9-11 tokens per second, developers can achieve high efficiency without the need for additional hardware investments. This matters because it democratizes access to powerful AI tools, allowing more developers to leverage advanced AI capabilities in their projects without incurring prohibitive costs. The ability to maintain a cool GPU temperature and quiet operation further underscores the practicality of this configuration for everyday use.

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