Local AI Coding Assistant: Run Your Own Copilot

Written by Jakub Rusinowski · Last updated 2026-08-04 · Hardware figures computed by our VRAM engine

A local AI coding assistant is three choices: a model (Qwen3-Coder, Devstral-2, Qwen 3.6 — picked by your VRAM), a tool (Continue for Copilot-style assist, Cline or Aider for autonomous agents, Tabby for teams), and the hardware to serve it. This hub covers all three, with every hardware figure computed by the same engine as our GPU checker.

Coding is the local-AI use case where the argument is strongest — code is the data companies least want leaving the building, coding assistants are subscription products you can actually replace, and 2026's open coding models are good enough that the replacement isn't a downgrade for everyday work.

It's also where hardware questions get sharpest. An autocomplete model must answer in milliseconds; an agentic model must hold a repo's worth of context; both must fit your VRAM at once. The guides below are organized so you can enter anywhere — by model, by tool, or by hardware — and each one links the others.

One cluster deserves calling out. Beyond picking a model and a tool there is the layer everyone discovers second: the loop the agent runs in, and the harness that runs it. On a frontier API you can get away with ignoring both. On your own GPU they are where most of the quality is — a verifier command, a raised context window and a rules file routinely move results further than a model-tier upgrade, and they cost no VRAM at all. That's the *Agent engineering* section.

The stack in 30 seconds

The guides

Start here

Pick a model, pick a tool, get something running today.

• Best Local Coding LLMs in 2026, by VRAM Tier — Every serious open coding model, ranked by the VRAM tier that actually runs it.
• Run a Local Coding Agent End-to-End: Ollama + Qwen3-Coder + VS Code — Ollama + Qwen3-Coder + Continue/Cline in VS Code, end to end, in ~40 minutes.
• Local GitHub Copilot Alternatives: What Actually Replaces It Offline — Continue, Tabby, Cline, Aider, Twinny — what each replaces, fully offline vs hybrid.
• Continue vs Cline vs Aider vs Tabby: the Local Coding Tool Matrix — The feature matrix: assistant vs agent vs terminal vs team server — and sensible pairings.

Hardware, speed & cost

What your card runs, what to buy, and why the agent feels slow.

• VRAM Requirements for Coding LLMs: What Every Tier Actually Runs — What 8, 12, 16, 24 and 48+ GB actually run — with the context-headroom rule that generic tables miss.
• Best GPU for Local AI Coding in 2026: Five Picks That Make Sense — Five GPU picks matched to the coding models each one runs — VRAM first, compute second.
• Local Coding Agent vs Copilot vs Cursor: What Each Really Costs — Local vs Copilot ($10/mo) vs Cursor ($20/mo) vs metered APIs — honest math, including where local loses.
• Why Your Local Coding Agent Feels Slow — and the Six Fixes That Work — Agent turns are prefill-bound: prompt-cache reuse, speculative decoding and the fixes that beat a GPU upgrade.

Agent engineering

The loop, the harness, the context and the tools — where local setups gain the most.

• Loop Engineering on a Local LLM: Agent Loops That Check Their Own Work — The act–observe–verify cycle: four loop levels, verifier design, iteration caps and the failure modes.
• The Agent Harness: What Actually Turns a Local Model Into a Coding Agent — What actually turns a model into an agent — the six jobs a harness does, and how to tune each locally.
• Context Engineering for Local Coding Agents: Budgeting a Window You Pay For — Budgeting a window you pay for in VRAM: what fills it, when to compact, and beating context rot.
• Tool Calling With Local Models: Making Agents Stop Breaking on Malformed JSON — Why local agents break on malformed JSON — chat templates, GBNF grammars and repair strategies.
• AGENTS.md for Local Coding Agents: The Cheapest Quality Upgrade You Can Make — The rules file that fixes small-model agents: what to write, what to cut, AGENTS.md vs CLAUDE.md.
• MCP With Local Coding Agents: Which Servers Earn Their Tokens — Which MCP servers earn their tokens on a 32K window — and when a shell command beats a server.

Run it safely, prove it works

Sandboxing, evaluation, and the read-only use case local models excel at.

• Sandboxing a Local Coding Agent: Autonomy Without the Blast Radius — Devcontainers, throwaway worktrees, permission allowlists and egress rules for unattended runs.
• Evaluating a Local Coding Agent: Benchmarks Lie, Your Repo Doesn’t — Benchmarks shortlist; your repo decides. Build a 20-task eval from your own git history.
• A Local Code Review Agent: The Use Case Small Models Are Actually Good At — The best quality-per-VRAM use case: read-only review, in a pre-commit hook or self-hosted CI.

Hands-on: specific models & tools

Per-tool and per-model setup, with the settings that actually matter.

• Run Qwen3-Coder Locally: 8B, 80B-A3B, and the 480B Question — The 8B, the 80B-A3B, the expert-offload trick, and when to just use the API for the 480B.
• Aider with Local Models: Setup and the Models That Actually Work — The terminal pair programmer on Ollama — config, context, and the models that follow its edit formats.
• Cline with Local Models: What Actually Works (and What Doesn’t) — The VS Code agent on local models — the context-window fix and the honest capability tiers.
• Terminal Coding Agents on Local Models: OpenCode, Qwen Code, Aider & Friends — OpenCode, Qwen Code and Aider pointed at your own GPU — config, model picks and honest limits.

Frequently asked questions

Keep going

• Local AI Report #2 — the open coding-model landscape (dated, citable)
• GPU compatibility checker — what your card runs
• Model library — full specs for every family
• Coding & debugging prompting workflow
• Enterprise & Sovereign AI — rolling out local AI company-wide