I need a coding assistant that runs locally on my laptop and does not send telemetry code snippets to the cloud.

To run a completely local, private, and offline coding assistant on your laptop with zero telemetry sent to the cloud, the gold standard approach is a two-layer stack : an inference engine (to run the AI model locally) paired with an IDE extension (to provide the UI, autocomplete, and chat interface).

The best tools and steps to set this up on your machine include:

1. The Local Inference Engine (Choose One)

• Ollama: Lightweight, runs natively in your system tray/background via CLI. Perfect for low resource overhead on a laptop.
• LM Studio: Offers a polished desktop app interface to search, download, and test models with a visual dashboard.

2. The IDE Extension (Choose One)

• Continue: The most popular open-source, model-agnostic coding assistant extension for VS Code and JetBrains. It connects seamlessly to Ollama or LM Studio out of the box and operates 100% locally.
• Tabnine: Offers an enterprise/local offline mode if you prefer an out-of-the-box traditional autocomplete experience.

3. Recommended Local Coding Models

Depending on your laptop's RAM/VRAM, grab one of these open-weights coding models via Ollama/LM Studio:

• DeepSeek-Coder-V2 Go to product viewer dialog for this item. or Qwen2.5-Coder Go to product viewer dialog for this item. (7B or 14B variants): Exceptional at code generation, debugging, and refactoring while fitting comfortably on modern developer laptops (16GB–32GB RAM).
• Llama 3 Code / Codestral : Great lightweight alternatives for inline autocomplete.

Step-by-Step Setup Guide (Using Ollama + Continue + VS Code)

bash``` ollama run qwen2.5-coder

Use code with caution.

json```
{
  "models": [
    {
      "title": "Qwen 2.5 Coder",
      "provider": "ollama",
      "model": "qwen2.5-coder"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen 2.5 1.5B Coder",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b"
  }
}

Use code with caution.

To help fine-tune this for your specific hardware, what are the specs of your laptop (RAM size and whether you have a dedicated Apple Silicon/NVIDIA GPU)? I can recommend the optimal model size so it runs fast without lagging your typing.

If your requirement is “the coding assistant must run on my laptop and must not send source code, snippets, or context to a cloud service”, look for a local LLM + local IDE integration setup rather than a hosted Copilot-style product. A few good options:

Best overall: Ollama + Continue (VS Code / JetBrains)

Ollama runs models on your machine, and Continue provides the editor integration. This combination gives you chat, code explanation, edits, and autocomplete-style workflows while keeping inference local.

Typical setup:

1. Install Ollama.
2. Download a coding model, for example: - qwen2.5-coder

• deepseek-coder
• codellama
• starcoder

3. Install Continue in your editor.
4. Configure Continue to use your local Ollama endpoint.

Pros:

• Free/open source
• Works offline after models are downloaded
• Good VS Code experience
• You choose the model

Cons:

• Quality depends heavily on your laptop hardware and model size

Best Copilot-like autocomplete: Tabby

TabbyML is designed as a self-hosted alternative to cloud coding assistants. It provides code completion and IDE integrations while allowing the model server to run on your own hardware.

Pros:

• Closest feel to inline “ghost text” completion
• Self-hosted architecture
• Good for teams that want internal deployment

Cons:

• More setup than Ollama + Continue

Best terminal coding agent: Aider-style workflows

For a terminal-first workflow, tools that connect to local models can edit files, inspect repositories, and help with refactoring while staying local. Local agents such as LocalCode are built around local model backends like Ollama or LM Studio.

Pros:

• Great for repo-level changes
• Works well with Git workflows
• Less tied to a specific IDE

Cons:

• More “AI pair programmer in terminal” than autocomplete

Hardware guidance

Approximate expectations:

• 8 GB RAM: small models only; usable but limited
• 16 GB RAM: good starting point (7B–8B coding models)
• 32 GB+ RAM: much better; can run larger models
• Dedicated GPU: significantly improves speed

Apple Silicon Macs generally do well because unified memory is efficient for local inference.

Privacy checklist

To make sure code stays local:

• Prefer models downloaded to disk.
• Bind the assistant to localhost.
• Disable any optional telemetry/analytics.
• Check the extension permissions in your IDE.
• If working with sensitive code, run with your network disabled and verify it still works.

My recommendation for most developers: Ollama + Continue + a 7B/14B coding model is the easiest private replacement for a cloud coding assistant. If you want the closest GitHub Copilot-style autocomplete experience, evaluate Tabby.

If you tell me your laptop specs (OS, RAM, CPU/GPU, and editor), I can suggest a specific model and setup.

You can build a fully private, offline coding environment by combining Ollama to run models locally and the Continue extension inside VS Code or JetBrains.

This setup ensures zero telemetry or code snippets leave your laptop.

Setup Workflow

1. Download and install Ollama for your operating system.
2. Open your terminal application and pull a coding model by running ollama run qwen2.5-coder:7b (or a smaller/larger variant depending on your laptop's RAM/VRAM).
3. Open your code editor (such as VS Code) and install the Continue Extension from the marketplace.
4. Open the Continue configuration file inside your editor and set your model provider to Ollama pointing to http://localhost:11434.
5. Disconnect your Wi-Fi to verify that autocomplete and chat operate completely offline.

Would you like help choosing a model size based on your laptop's RAM or GPU VRAM , or instructions for setting up tab-autocomplete locally?

Yes. If your requirement is “the code snippets and prompts must stay on my laptop”, I’d use a local-model setup rather than a cloud coding assistant.

Best overall: Continue + Ollama

continue.dev is an open-source coding assistant for VS Code/JetBrains, and ollama.com runs the LLM locally.

A good architecture is:

VS Code → Continue → Ollama → local coding model

This can operate completely offline. Continue specifically documents an offline/air-gapped setup and says to disable anonymous telemetry and configure a local model.

For coding, Continue currently recommends local Qwen Coder variants for autocomplete, among other models.

Other good choices

• Aider + Ollama — excellent if you prefer a terminal/CLI workflow. Aider's analytics are opt-in, and it provides an explicit --analytics-disable option.
• Cline + Ollama — better if you want an autonomous agent that can modify files and execute commands. Cline supports local models through Ollama/LM Studio. Its telemetry is optional, but I'd disable it for a strict privacy setup.
• LLM Local Assistant — a VS Code extension specifically designed around local/offline operation with Ollama, LM Studio, or vLLM.

What I'd choose

One important distinction: “local model” and “no telemetry” are separate requirements. Even if the LLM itself is running locally, an editor extension could theoretically send usage telemetry elsewhere. For genuinely sensitive source code, configure the assistant with telemetry disabled and ideally block its outbound network access. Continue's current documentation explicitly supports running without Internet access.

If you tell me your laptop OS (Windows/macOS/Linux), RAM, and whether you have an NVIDIA/AMD GPU or Apple Silicon, I can recommend a specific local model and setup that should run well on it.

If you want a coding assistant that runs entirely on your laptop and does not send code snippets or telemetry to a cloud service, look for a setup where:

• The LLM runs locally (not just the editor UI).
• The editor talks to localhost (for example, Ollama/llama.cpp/LM Studio).
• Telemetry, analytics, and cloud sync are disabled or absent.
• You can inspect the source/configuration if privacy is critical.

Good options:

1. VS Code + Ollama + local coding model (most flexible)

A common privacy-first stack:

• Editor: Microsoft Visual Studio Code
• Local model runtime: Ollama
• Coding models: Qwen Coder, DeepSeek Coder, Code Llama, StarCoder, etc.
• Extension: a local-only assistant extension

Your code stays on your machine because the model inference happens locally. Projects such as LLM Local Assistant and Ollama-based VS Code extensions are designed around this approach.

Typical setup:

1. Install Ollama.
2. Download a coding model: ``` ollama pull qwen2.5-coder

3. Install a VS Code extension that connects to Ollama.
4. Disable VS Code telemetry if required.

Best if you want a normal IDE experience.

## 2. Continue + Ollama (open-source, popular)

Continue is a VS Code/JetBrains assistant that can connect to local models. It is a good choice if you want:

- chat about your codebase
- inline edits
- local embeddings/search
- model switching

You control the model endpoint rather than sending prompts to a hosted service.

## 3. Zed Industries editor with local models

Zed supports local AI workflows and can be paired with local model servers. Good if you want a lightweight, fast editor.

## 4. Fully local coding agents

If you want something closer to Cursor/Claude Code-style agents:

- [localcode.codes](https://www.localcode.codes/?utm_source=chatgpt.com) — designed around local models through Ollama or LM Studio, with no cloud account required. [LocalCode](https://www.localcode.codes/?utm_source=chatgpt.com)
- [agentaleph.com](https://agentaleph.com/?utm_source=chatgpt.com) — a local coding agent that runs models on your hardware and advertises no telemetry/cloud dependency. [Agent Aleph](https://agentaleph.com/en/?utm_source=chatgpt.com)
- [github.com](https://github.com/haisher/local-coding-agent?utm_source=chatgpt.com) — an example of wiring a coding agent to local Ollama models. [GitHub](https://github.com/haisher/local-coding-agent?utm_source=chatgpt.com)

## Hardware expectations

Rough guide:

- **8 GB RAM:** small models (3B–7B) are realistic, slower.
- **16 GB RAM:** good for 7B–14B coding models.
- **32+ GB RAM or a GPU:** better experience with larger models.

For coding quality, a local 7B–14B coder model can be useful, but cloud models are still generally stronger for very complex architecture/debugging tasks.

## Privacy checklist

Before trusting any assistant:

- Check for a "cloud mode" or "remote inference" toggle.
- Block outbound network access if you need guarantees.
- Disable telemetry in the editor and extension.
- Prefer open-source tools you can inspect.
- Verify the model endpoint is `localhost` (e.g., `127.0.0.1`), not a vendor API.

If you tell me your laptop specs (OS, RAM, GPU/Apple Silicon model), I can suggest a local model and setup that should run comfortably.

Here are top web results for exploring this topic:

Localtonet·https://localtonet.com How to Run a Local AI Coding Assistant That Never Sends Your ...Step 1: Install Ollama. Linux. curl -fsSL https://ollama.com/install.sh | sh # Verify it is running ollama --version systemctl status ollama Bash · Step 2: Pull the Right Models. You need two types of Reddit·https://www.reddit.com**Is** there an AI coding agent that works locally on something like ...13 votes, 23 comments. I'm tired of paying for coding agents, IDEs or what so ever, and I need something that I can use freely -or at least… YouTube·https://www.youtube.com**Run** AI Locally on Your Laptop (No Subscription Needed !) - YouTube Let me show you how you can run AI locally in a few minutes. Check out Coder Agents: https://fandf.co/4u0qz9e According to a recent report from the Cloud Security Alliance, 20% of companies have alrea Iternal AI·https://iternal.ai Self-Hosted AI Coding Assistant : 10 Best (2026) - Iternal Technologies A security-first roundup of private, on-premises, and air-gapped AI coding tools — ranked for enterprise, IT, and regulated software teams that cannot send source code to the cloud. self-hosted AI cod Lekh AI·https://lekhai.app Best Local AI Coding Assistants for Developers (2026) - Lekh AI Blog An AI coding assistant is "local" when the model, your code, or both stay on your own machine instead of a cloud server, and which one determines how private your setup actually is. There are three di Bodega One·https://www.bodegaone.ai AI coding tools that work completely offline (2026)... for AI-assisted coding in 2026: Bodega One Code (full IDE + agent), Aider (CLI), Cline (VS Code plugin), and Void (VS Code fork, now archived). All require a local model running via Ollama, LM Stu PromptQuorum·https://www.promptquorum.com Best AI Coding Assistant for Local LLM 2026 - PromptQuorum Contrarian Take: When Local LLM Coding Assistants Are the Wrong Choice. Local LLM coding assistants aren't always the right answer. Here's when to use cloud instead: You don't have GPU hardware. Local Medium·https://medium.com**Running** AI Coding Assistants Locally — Lessons Learned - Medium Here's the most important lesson: local AI for coding isn't always the answer. Local models excel at: Simple scripts and small projects; Privacy-sensitive code you can't send to the cloud; Learning an Virtualization Review·https://virtualizationreview.com Ditch the Cloud : 7 Powerful, Free Local AI Tools It is aimed at users who want an AI coding assistant that is more transparent and configurable than a closed commercial tool. Its commercial competition includes Claude Code, OpenAI Codex and GitHub C NVIDIA Blog·https://blogs.nvidia.com**Run Coding Assistants** for Free on RTX AI PCs | NVIDIA Blog ... researching examples, setting up boilerplate code, authoring code with appropriate syntax, tracing down bugs and documenting functions. These are essential tasks that can take time away from probl

To run a powerful, 100% private coding assistant locally on your laptop with zero telemetry or cloud code-snippet logging, the industry standard stack pairs a local inference engine with an open-source IDE extension or terminal agent.

The Recommended Local Stack

1. The Inference Engine: Ollama (Best for background service and command-line management) or LM Studio (Best if you prefer a graphical interface to browse and load models).
2. The Editor Extension: Continue (Open-source assistant for VS Code and JetBrains) or Cline (For autonomous multi-file agent tasks).
3. The Terminal Alternative: Aider (A git-aware, terminal-based coding assistant that edits local files directly).

Step-by-Step Setup Guide (VS Code + Ollama + Continue)

1. Install and Run Ollama
   • Download and install Ollama for your operating system.
   • Open your terminal and pull a specialized, high-performance local coding model like Qwen2.5-Coder or Qwen3-Coder:bash``` ollama run qwen2.5-coder:7b

Use code with caution.*(This downloads a versatile 7B model suitable for general chat and editing. If your laptop has limited RAM/VRAM, you can use the smaller 1.5B version for fast inline completions).* [](https://www.youtube.com/watch?v=p1_MPhRIJxo) [[1]](https://www.youtube.com/watch?v=p1_MPhRIJxo)[[2]](https://www.youtube.com/watch?v=us6KNXcbUvc)
2. **Install the Continue Extension** 
	- Open VS Code, go to the **Extensions** marketplace, and install the Continue Extension.[](https://www.youtube.com/watch?v=p1_MPhRIJxo) [[1]](https://www.youtube.com/watch?v=T_arXqLvOKs)
3. **Connect Continue to Your Local Ollama Instance** 
	- Click the Continue icon in your sidebar.
	- Open the `config.json` file inside the extension settings and add your local Ollama model:json```
{
  "models": [
    {
      "title": "Qwen2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 1.5B",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b"
  }
}

Use code with caution. - Once saved, all token generation happens locally at http://localhost:11434 . No data, snippets, or telemetry ever leave your laptop.

If you'd like to tailor this setup, tell me:

• Your laptop's RAM/VRAM specifications and whether it's an Apple Silicon Mac or Windows/Linux with an NVIDIA GPU
• Whether you primarily want inline tab-autocomplete , a sidebar chat , or an autonomous multi-file editing agent

I can recommend the optimal model size and configuration for your hardware.

Yes. If your requirement is “my source code and prompts must stay on my laptop”, I’d recommend a local-model stack rather than a cloud coding assistant.

My top pick: Continue + Ollama

Continue is an open-source coding assistant that integrates with VS Code/JetBrains, while Ollama runs the AI model locally. Continue explicitly documents an offline/air-gapped configuration where you can disable anonymous telemetry and use only local models.

A privacy-oriented setup would be:

VS Code → Continue → Ollama → local coding model

Your code is processed by the model running on your machine rather than sent to an API provider. Ollama says that locally run prompts, responses, and model interactions aren't transmitted to Ollama.

For stronger isolation, Ollama also has a local-only mode:

{
  "disable_ollama_cloud": true
}

or:

OLLAMA_NO_CLOUD=1

That disables Ollama's cloud features entirely.

Other good options

Cline can use Ollama or LM Studio locally. Its telemetry is enabled by default, but its documentation says it can be disabled; if you're particularly strict about privacy, I'd turn it off.

Aider is another particularly nice choice if you like the terminal. It connects directly to local Ollama models, and its analytics are opt-in; you can explicitly disable them with aider --analytics-disable.

One important caveat

“Local model” and “zero telemetry” are separate things. You want both:

1. Model inference stays local — Ollama rather than OpenAI/Anthropic/etc.
2. Assistant telemetry is disabled — e.g. Continue's anonymous telemetry.
3. Cloud functionality is disabled — particularly useful with Ollama's OLLAMA_NO_CLOUD=1.
4. No external model/API providers in your configuration.

Continue's current documentation also describes development-data collection, so I'd explicitly review/disable its data configuration rather than assuming “local model” automatically means “nothing leaves the machine.”

If you tell me your laptop's OS (Mac/Windows/Linux), RAM, and whether it has an NVIDIA/Apple GPU, I can recommend a specific local coding model and setup that should run well on it.

The gold standard stack for a 100% private, locally-run AI coding assistant that keeps your code entirely on your machine—with zero telemetry or cloud syncing—combines an inference engine, an open-source IDE extension, and a privacy-focused editor.

The Recommended Local Stack

1. The Inference Engine: Ollama or LM Studio
   • Ollama runs smoothly as a lightweight background service in your system tray, serving models locally over http://localhost:11434.
   • LM Studio provides a clean graphical interface to discover, download, and manage quantized weights directly from Hugging Face.
2. The Coding Extension: Continue.dev
   • The leading open-source AI code assistant for IDEs. It plugs straight into your editor, offering inline tab-autocomplete, chat panels, and codebase indexing—running entirely against your local Ollama or LM Studio endpoint.
3. The Editor (Optional, for ultimate tracking prevention): VSCodium
   • If you use Visual Studio Code, Microsoft includes built-in telemetry. VSCodium is a free, 100% clean, open-source binary build of VS Code with all Microsoft telemetry and tracking stripped out completely.

Step-by-Step Setup Guide

1. Install and Start the Inference Engine
   • Download and install Ollama for your operating system.
   • Open your terminal and pull a capable local coding model (e.g., Qwen-Coder or DeepSeek-Coder variants depending on your RAM/VRAM capacity):bash``` ollama pull qwen2.5-coder:7b

Use code with caution.
	- *Tip:* For fast inline autocomplete, pull a smaller model (like a 1.5B or 3B parameter variant) alongside your main chat model.[](https://www.sitepoint.com/local-ai-coding-assistant-vscode-ollama-continue/) [[1]](https://www.youtube.com/watch?v=UZC4sTV-THA&vl=en&t=312)[[2]](https://llmconfigurator.com/en/guides/coding-agents/setup-local-coding-agent)[[3]](https://www.youtube.com/watch?v=cskiAgr78fs)
2. **Install the Extension** 
	- Open VS Code (or VSCodium).
	- Go to the Extensions marketplace, search for **Continue** , and install it.[](https://www.sitepoint.com/local-ai-coding-assistant-vscode-ollama-continue/) [[1]](https://dev.to/manikandan/how-to-use-ai-models-locally-in-vs-code-with-the-continue-plugin-with-multi-model-switching-3na0)
3. **Configure Continue for Local Use** 
	- Open the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P` ) and select **Continue: Open Config File**.
	- Edit the `config.json` file to route requests exclusively to your local Ollama instance:json```
{
  "models": [
    {
      "title": "Qwen2.5 Coder (Local)",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 1.5B (Local)",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b"
  }
}

Use code with caution.

To help fine-tune this setup for your specific hardware, what are your laptop's RAM/VRAM specifications and GPU model (e.g., Apple Silicon M-series, Nvidia RTX, etc.)? I can recommend the optimal model size so you get fast completions without choking your system.

If your requirement is “the model must run on my laptop and source code/context must not leave the machine”, you’ll want a local LLM + local coding frontend rather than a cloud IDE assistant.

Good options:

A common private setup:

1. Install a local model runtime:

   • Ollama (simpler)
   • llama.cpp (more configurable)

2. Download a coding model:

   • Qwen Coder family
   • DeepSeek Coder family
   • StarCoder family
   • Code Llama variants

3. Connect it to your editor:

   • VS Code + Continue
   • VS Code + llama.vscode
   • Neovim plugins if you prefer terminal workflows

4. Disable network access if you need a stronger guarantee:

   • Run the model server bound only to localhost
   • Use firewall rules to block outbound connections for the assistant process
   • Avoid extensions that require accounts or cloud APIs

Hardware guidance:

• 8–16 GB RAM: smaller 3B–7B coding models are realistic.
• 16–32 GB RAM: 7B–14B models are usually comfortable.
• 32 GB+ RAM or a good GPU: larger coding models become practical.

For a balance of quality and privacy on a typical laptop, I’d start with:

Ollama + a Qwen/DeepSeek coding model + Continue or llama.vscode in VS Code.

Before trusting any tool in a sensitive environment, check:

• whether telemetry can be disabled,
• whether extensions make external requests,
• where chat history/indexes are stored,
• whether embeddings are generated locally.

Some VS Code local assistants explicitly state that they avoid cloud calls and telemetry when configured for local models, but you should still verify the specific extension version and settings you install.