The Best Local AI Coding Assistants That Run Offline

What makes a coding assistant truly local?

A local assistant has two parts that both run on your hardware: the model server and the editor or terminal client. If either one calls a cloud API, your code leaves the machine.

Most open source clients are model-agnostic. They can talk to hosted APIs, but they also accept an OpenAI-compatible endpoint on localhost, which is what Ollama, LM Studio, llama.cpp’s server and vLLM expose.

Check telemetry settings as well. Some tools send anonymous usage data by default; offline setups should disable it or block outbound traffic entirely.

Which local coding assistants are worth using?

Which model should you run locally for code?

Use models trained or tuned for code. Families such as Qwen’s coder releases, DeepSeek’s coder models, Mistral’s Codestral and code-tuned Llama derivatives all publish open weights; read each licence, because terms differ, and some Codestral releases use a non-production licence.

Autocomplete and chat want different models. Autocomplete needs a small, fast model that supports fill-in-the-middle, typically 1.5–7B. Chat and agentic edits benefit from the largest coding model your memory allows, often 14–32B at 4-bit on a 24 GB GPU.

Agent tools like Cline and Aider rely on reliable instruction following and edit formats. Small models often produce malformed diffs, so test a candidate on your own repository before trusting it with multi-file changes.

What hardware do you need for offline coding help?

Context length costs memory. Agent tools send many files per request, so leave headroom for long prompts rather than filling memory with the largest possible model.

• Autocomplete only: any modern laptop with 16 GB RAM runs a 1.5–3B code model through Ollama with acceptable latency.
• Chat plus autocomplete: a 12–16 GB GPU or Apple Silicon with 32 GB unified memory handles a 7–14B chat model and a small completion model together.
• Agentic edits on real projects: a 24 GB GPU or 48 GB+ unified memory for 30B-class coding models with long context.
• Team server: one workstation GPU running Tabby or vLLM can serve completions to several developers on the local network.

Continue vs Cline vs Aider: how do they differ?

Continue feels like a classic assistant inside the editor: a chat sidebar, highlight-and-edit, and tab autocomplete. You can assign different local models to chat, edit and autocomplete, which makes it the most flexible general choice.

Cline acts as an agent. It reads files, proposes edits, runs terminal commands and asks you to approve each action. It shines with strong models; with small local models, keep tasks narrow and specific.

Aider lives in the terminal and treats git as the source of truth. You add files to the chat, describe the change and Aider commits each edit, so rolling back is a single git command. It publishes guidance on which models handle its edit formats well.

Try at least two clients for a week each. The differences are mostly about workflow feel, such as sidebar chat versus terminal versus approval-driven agent, and that is personal enough that reviews rarely settle it for you.

Whichever you choose, keep git as your safety net: commit before asking for large changes, review diffs line by line and revert freely.

How do you give a local model enough context about your codebase?

Local models have smaller effective context than cloud models, so what you send matters more. Continue and Tabby can index your repository and retrieve relevant snippets, while Aider builds a compact repository map of files and symbols to orient the model.

Help them with explicit project rules: the framework, folder layout, testing commands and conventions. A short rules file saves the model from guessing and noticeably improves suggestions from mid-size models.

Keep tasks scoped to a few files at a time. Asking a 14B model to refactor an entire service in one request usually fails, while the same work split into five focused requests often succeeds.

• Use embeddings-based code search where the client supports it, with a small local embedding model.
• Add only the files that matter to the chat instead of the whole repository.
• Keep a short architecture note in the repository that the assistant can always read.
• Prefer smaller, reviewable diffs so mistakes are easy to spot and revert.

How to set up a local coding assistant for you or your team

Different working styles call for different combinations, and nothing stops you from running two clients against the same local model server. Aider in a terminal and Continue in the editor can share one Ollama instance without conflict.

Teams have one extra option: a shared GPU box on the office network running Tabby or vLLM, so laptops stay light while code still never leaves the building. That setup also centralizes model upgrades, which keeps everyone on the same tested version.

• Install Ollama or LM Studio and pull one small code model for completion and one larger coding model for chat.
• Confirm the server responds on localhost and note the model names exactly as the server reports them.
• Install your client, point it at the local OpenAI-compatible or Ollama endpoint and disable any cloud providers.
• Turn off telemetry in the client and consider a firewall rule that blocks outbound traffic from the editor.
• Add project rules or context files describing your stack and conventions so the model has less to guess.
• Try a real task, such as adding a tested function, and compare two models before settling.

Where local coding assistants fall short

Local models trail frontier cloud models on long, multi-step refactors across large codebases. Expect more retries, more manual guidance and smaller task sizes.

• Using a chat model for autocomplete, which is slow and lacks fill-in-the-middle support.
• Choosing a model too large for memory, forcing CPU offload and multi-second latency per suggestion.
• Default context windows that silently truncate files; set the context length explicitly in the server.
• Letting an agent run commands without approval on a machine with real credentials.
• Judging a tool by one bad session with an unsuitable model instead of testing a proper coding model.

Is offline coding worth the trade-offs?

It is worth it when code must not leave the building, when you work on planes or restricted networks, or when you want predictable cost with no per-seat or per-token billing. Autocomplete in particular runs very well locally today.

Many developers mix: a local model for completion and quick questions, and a cloud model for the hardest agentic tasks on non-sensitive code. Because the leading clients are model-agnostic, switching is a settings change. RepoLoot’s catalog tags coding tools by licence, which helps when your employer restricts what you can install.

Frequently asked questions

What is the best offline alternative to GitHub Copilot?

Continue or Tabby with a small local code model gives the closest Copilot-style experience: tab autocomplete plus chat inside the editor. Run the model with Ollama, LM Studio or llama.cpp. Tabby adds a self-hosted server that a whole team can share over the local network.

Can Cline or Aider work with local models?

Yes. Both accept local endpoints from Ollama, LM Studio or any OpenAI-compatible server. Results depend heavily on the model, because agentic editing needs reliable instruction following. Use the largest coding-tuned model your hardware can run and keep tasks small and well described.

How much RAM do I need for a local coding assistant?

About 16 GB of system memory is enough for autocomplete with a 1.5–3B model. For a capable chat model alongside it, aim for a 12–24 GB GPU or 32 GB or more of unified memory on Apple Silicon. Agentic workflows with long context benefit from even more.

Are local AI coding assistants really private?

They can be fully private if both the model server and client run locally and telemetry is disabled. Check each tool’s settings, avoid configuring cloud fallback providers, and optionally block outbound network traffic for the editor to be certain no code or prompts leave your machine.