The Best LOCAL Agentic Coding Workflow (Complete Guide) — AI Summary

Summary

Run powerful AI coding assistants locally using LM Studio and VS Code. Bypass subscription fees and cloud latency by configuring models like Qwen 2.5 to handle autocomplete and agent tasks entirely on your hardware.

Key Topics

• Quantization: Quantization reduces the precision of a model's weights, which drastically lowers the memory footprint. This is essential for local usage because it allows large models to fit into consumer GPU VRAM, enabling higher speeds and better performance on non-enterprise hardware.
• Unified Memory: Unified memory allows the GPU to utilize the system's main RAM directly, which provides a massive advantage for running large models on laptops that don't have dedicated, high-capacity VRAM graphics cards.
• Agent Tool Use: For an AI to be a coding agent, it must be trained to recognize and execute functions—like creating or editing files—when requested. Without 'tool-use' capability, a model is merely a chat bot, not an editor.

Key Takeaways

• Check your GPU VRAM or unified memory and download LM Studio.
• Configure Qwen 2.5 Coder for autocomplete in VS Code.