Best Local LLM for Coding: 8GB to 24GB VRAM Picks

This guide explores the best local Large Language Models (LLMs) for coding, categorized by GPU VRAM capacity. For users with 8GB to 24GB of VRAM, the author recommends specific models from the Qwen2.5 and Qwen3 Coder families, which offer high performance for autocomplete and refactoring tasks without the cost of cloud-based APIs. The article emphasizes that model selection should be prioritized by VRAM availability, suggesting that users drop quantization levels before reducing model size. It provides a practical breakdown of hardware requirements, including a rule of thumb for VRAM usage based on context length. Additionally, the piece includes a step-by-step guide for setting up these models using Ollama, enabling developers to run powerful coding assistants locally. By leveraging local models for routine tasks, developers can maintain privacy and eliminate subscription fees, reserving cloud-based frontier models for complex, multi-file reasoning.