Local Agentic Coding Experiment on MacBook Air Takes 63 Hours
A developer successfully generated a basic, functional flight simulator in HTML using a 3-bit quantized Qwen 27B model run locally on a MacBook Air M2. However, the process required a staggering 63 hours to complete due to hardware limitations and a large 57k context window. This experiment highlights the current performance bottlenecks of running large local LLMs for complex, multi-step agentic coding tasks on consumer-grade hardware. It demonstrates that while local execution is possible, cloud-based APIs remain vastly superior in speed and output quality for such workloads. The local setup used LM Studio with the Qwen 27B Q3_K_S model, whereas cloud alternatives like Google AI Studio and Qwen Studio completed the same task in 20 minutes and 2 hours, respectively, with better visual results. The first prompt alone took 47.8 hours to process locally before a second debugging prompt resolved a key input issue.
## BACKGROUND
Agentic coding refers to using AI agents that can autonomously plan, write, and debug code across multiple steps rather than just completing single lines. Running these models locally often requires quantization, such as the Q3_K_S format, which compresses the model size to fit into consumer RAM at the cost of some reasoning accuracy. Tools like LM Studio provide a user-friendly interface to run and manage these quantized models directly on personal computers.