The Local AI Coding Setup Everyone Asked For

2026-06-27 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The article, published on June 19, 2026, details a specific local AI coding setup, responding to reader inquiries about hardware and models used in a previous piece on replacing cloud-based AI. It outlines the author's laptop specifications, Ollama setup, Qwen model choice, and VS Code workflow, emphasizing what runs effectively locally. The author highlights that local AI offers significant benefits like privacy, control, cost efficiency, and offline access, serving as a viable alternative to cloud solutions. Crucially, the piece underscores that the practical experience of local AI, whether with a 3B model on a laptop or a 70B model on a workstation, is profoundly dependent on the underlying machine's RAM, VRAM, CPU, and SSD capabilities.

Key takeaway

For AI Engineers evaluating local large language models, you must prioritize understanding the specific hardware requirements for your chosen models. Your machine's RAM, VRAM, CPU, and SSD directly dictate the practical performance and feasibility of running local AI for tasks like coding assistance. Ensure your setup can handle the desired model size to fully realize benefits such as enhanced privacy, cost control, and offline capabilities.

Key insights

Local AI performance and experience are critically dependent on specific hardware specifications.

Principles

• Local AI offers privacy, control, cost savings, and offline access.
• Hardware details are essential for evaluating local AI viability.
• Model size and machine specs dictate real-world local AI experience.

In practice

• Consider Ollama for local LLM management.
• Explore Qwen models for local coding assistance.
• Integrate local AI within VS Code workflows.

Topics

• Local AI
• LLM Inference
• Hardware Specifications
• Ollama
• Qwen Model
• VS Code

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.