How to Build a Local AI Coding Assistant Stack (Full Open-Source Copilot Alternative)

TL;DR for busy founders

• Qwen3-1.7B → easiest to start with; runs on mid-range GPUs (8-12GB VRAM) but slower generation; perfect for learning local AI coding.
• Qwen3-32B → sweet spot for serious coding; needs RTX 4090 (24GB VRAM) but matches ChatGPT quality; ideal for daily development work.
• VS Code + Continue/Cline → essential extensions for local AI coding; Continue for chat/completion, Cline for autonomous file editing and commands.
• Goose framework → advanced agentic AI that can run tests, install packages, and edit code autonomously; works with any local model.

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What components make up a local AI coding assistant?

A local AI coding assistant stack combines your code editor, one or more AI-driven extensions, and a locally running Large Language Model (LLM). In practice, this means using an IDE like Visual Studio Code with AI extensions (such as Continue or Cline) and running an open-source code LLM (like Qwen or Code Llama) on your own hardware.

These components work together to mimic what cloud tools like GitHub Copilot do – but entirely on your machine, keeping your code private and avoiding subscription fees. By configuring the VS Code extensions to use your local model, you get features like code completion, chat-based help, and even autonomous coding actions, all without an internet connection.

For a broader founder stack including marketing and support agents, see our AI Tools For Solo-Founders: Complete Stack Guide.

How can VS Code Continue and Cline enable AI coding locally?

Continue and Cline are two popular open-source VS Code extensions that bring AI assistance to your editor. Continue is essentially a ChatGPT-style coding assistant inside VS Code, providing in-editor chat and smart autocomplete suggestions (Continue GitHub).

You can ask it questions about your code, get explanations, or have it generate snippets. It’s designed to integrate with various models – including local ones – via providers like Ollama or LM Studio. For example, you can configure Continue to use a local model (like Code Llama or Qwen) by pointing it to a localhost API, giving you AI help without any cloud API key.

Cline, on the other hand, is an “autonomous coding agent” extension. It not only chats about code but can also create or edit files, execute shell commands, and even browse the web on its own when given high-level instructions (Cline GitHub).

Cline operates in dual modes – a planning mode to outline steps, and an action mode to carry them out – essentially acting like an AI pair programmer that can operate your IDE. To use Cline for free, many developers connect it to open model APIs (e.g. via OpenRouter) or local LLM servers.

For instance, you can plug Cline into a free API endpoint for a Llama 2-based model or point it to a local model server; either way, Cline itself is free and doesn’t require any proprietary service. By installing these extensions and configuring them to use local models, your VS Code becomes an AI-enabled development environment without relying on cloud services.

For a hands-on comparison of in-editor vs cloud agent workflows, see our Cursor vs Codex: Choosing the Right AI-Assisted Coding Tool.

What is codename Goose and how does it help with local AI code assistance?

Codename Goose (often just Goose) is another open-source tool that goes beyond the editor-specific extensions – it’s an AI agent framework you can run locally to assist with coding tasks. Goose acts like a personal AI that can install packages, run tests, edit code, and more by orchestrating an LLM with tool use (Goose GitHub).

Think of it as a layer on top of any LLM that enables autonomous actions in your development workflow. For example, you can tell Goose (in natural language) to “create a Python function for X and write a unit test,” and it will use the AI model to generate the code, create the file, and even run the tests automatically.

Goose is extremely flexible: it’s designed to work with any LLM of your choice – you just configure your provider or local model in its settings. This means you can pair Goose with the latest Qwen model running on your machine, and Goose will drive it to perform coding tasks autonomously.

One developer demonstrated using Goose with a quantized 8B Qwen model fully offline; even this smaller model was able to do multi-turn tool usage like killing processes and fixing ports on command. In summary, Goose provides a more agentic, possibly multi-step AI helper compared to the simpler autocomplete/chat of VS Code plugins.

It’s ideal if you want your AI to take actions (under your supervision) in a local environment. And since Goose runs on your machine and can work with local models, it aligns perfectly with a privacy-first, no-cloud setup.

For more on agentic AI and how it fits into a founder’s workflow, see AI Tools For Solo-Founders: Complete Stack Guide.

Which open-source LLM works best for coding tasks?

Several open-source LLMs have been trained or fine-tuned for coding, and the leader among them in 2025 is Qwen3 from Alibaba. This is the latest generation of large language models in the Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models with groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support (Qwen3-1.7B, Qwen3-32B, Qwen3-235B-A22B).

Qwen3-235B-A22B is the flagship model, featuring a massive 235 billion parameters with 22 billion active parameters using MoE architecture. This model delivers exceptional performance across coding, reasoning, and general tasks while being more efficient than traditional dense models of similar size (source).

Qwen3-32B provides excellent performance for most coding tasks with 32 billion parameters and a 32,768 token context window. This model strikes the perfect balance between performance and hardware requirements, making it ideal for developers with high-end GPUs (source).

Qwen3-1.7B is the most accessible option, requiring only 1.7 billion parameters while still delivering impressive coding capabilities. This model can run on mid-range hardware and is perfect for developers getting started with local AI coding assistants (source).

All Qwen3 models feature seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within a single model. This ensures optimal performance across various scenarios (Qwen3-1.7B).

The models support 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. They excel in agent capabilities, enabling precise integration with external tools in both thinking and non-thinking modes (Qwen3-1.7B).

Aside from Qwen3, notable open models include Code Llama (from Meta) and derivatives (like Replit’s CodeInstruct, WizardCoder, etc.), StarCoder (Hugging Face), and newer entrants like Kimi-K2. Many of these can be used with the VS Code extensions or Goose.

Qwen3 tends to lead in logic and code generation quality, while Code Llama is a solid general coder available in various sizes (7B, 13B, 34B). If you have limited hardware, you might choose a smaller model like Qwen3-1.7B – for instance, it can run on a mid-range GPU – but you’ll get more limited assistance.

On the other hand, using a larger model like Qwen3-32B or Qwen3-235B-A22B will give better results, closer to ChatGPT or Claude, at the cost of needing a high-end machine. The good news is these models are improving fast and closing the gap with commercial models, so a fully open-source coding assistant is no longer a distant dream – it’s here and getting better every month.

For a founder’s perspective on choosing the right coding tool for your workflow, see What AI Coding Tool to Use? A Sincere Founder-to-Founder Opinion for 2025.

What PC hardware is recommended for running local AI models?

Running large AI models locally does demand a powerful setup, especially if you want speed. The key components are GPU memory, system RAM, and fast storage. For the latest Qwen3 models, hardware requirements vary significantly based on model size.

For Qwen3-235B-A22B (the flagship MoE model), you’ll need enterprise-grade hardware with multiple high-end GPUs. This model requires significant computational resources and is best suited for research institutions or companies with dedicated AI infrastructure.

For Qwen3-32B, experts recommend a GPU with at least 20–24 GB of VRAM (video memory). An NVIDIA RTX 4090 (24GB) is an excellent choice for this model. With 24GB, you can comfortably load the model in 4-bit quantized form for optimal performance.

For Qwen3-1.7B, you can get started with much more modest hardware. This model can run on GPUs with 8–12 GB of VRAM like an RTX 3070 or RTX 4060. It’s possible to run it on even smaller GPUs using optimized runtimes and lower precision, though generation will be slower.

In addition to the GPU, you’ll want plenty of system RAM – at least 32 GB for larger models, but 16 GB can work for Qwen3-1.7B. This helps with model loading overhead and keeping the OS and other apps running smoothly alongside.

A fast NVMe SSD is also important, because loading these models (which can be tens of gigabytes in size) from disk can be the slowest part – an NVMe drive ensures quick data throughput.

One example setup from a developer was a MacBook M1 Pro with 64GB unified memory; it could run Qwen3-1.7B locally at acceptable speed, though larger models were off-limits. In general, if you don’t have top-tier hardware, you can still experiment with local AI by using Qwen3-1.7B or adopting a hybrid approach (run a smaller model locally for most tasks, and only use cloud for very heavy tasks).

The bottom line: the better your hardware, the smoother and faster the AI assistance – but even a mid-range PC can get you started with Qwen3-1.7B, just expect a bit of patience with generation times.

What are the pros and cons of a local AI coding stack?

Going local with your AI coding assistant has some clear advantages:

Privacy and Security: Your code and prompts never leave your machine, which is a huge plus for proprietary or sensitive projects. Cloud services send your code to third-party servers, but a local model means everything stays in-house. This eliminates worries about leaking API keys or data to an external API.

Cost Efficiency: While you might invest in hardware, you avoid the ongoing monthly fees. Services like Copilot or Cursor charge $10–$20+ per month, which adds up. Running an open model is essentially free after the hardware investment. If you already have a decent GPU, using it for AI can save money in the long run.

Offline Availability: A local setup works offline. Whether you’re on a train with spotty internet or simply want to code from a cabin in the woods, your AI buddy is always available. No internet reliance also means lower latency – generation happens on your GPU with no network delays.

Control and Customization: You have full control over the AI. You can choose which model to use (and can switch or fine-tune it), how it’s prompted, and even modify the extension’s source code if you desire. Open-source tools are highly customizable to fit your workflow.

However, there are some trade-offs to consider:

Upfront Hardware Cost: To get a smooth experience, you might need to spend on a high-end GPU or upgrade your RAM. Not everyone has a 24GB GPU at their disposal. In contrast, cloud tools run on powerful servers so you pay a smaller monthly fee instead of a big hardware purchase.

Setup Complexity: Cloud solutions are plug-and-play (you install an extension and log in). A local stack requires more tinkering – installing and configuring models, possibly dealing with Docker or command-line setup (for things like Ollama or Goose), and ensuring compatibility. It’s a bit technical, though guides and community resources are abundant to help.

Model Quality Gaps: While open models are improving rapidly, the absolute cutting-edge models (GPT-4, Claude 2, etc.) are still proprietary. You might find that a local 13B model occasionally writes less elegant code or misses context that a larger cloud model would catch. The gap is closing, but it’s something to remember – the very best AI coding performance still tends to come from the largest models on the cloud.

Maintenance: When you self-host, you are also the sysadmin. You’ll need to update models or software manually to get improvements. With a service, updates happen behind the scenes. Also, if something breaks (for example, a model not loading), you’ll be troubleshooting it yourself with community help.

In summary, a local AI coding assistant can absolutely replicate much of the Copilot/Cursor experience today, and even offer things those cloud tools can’t (like total privacy and offline use). But it comes with responsibilities in setup and maintenance, and you’ll need sufficient hardware to make it truly shine. If you’re a developer who values control and is willing to put in a bit of initial effort, the payoff is an AI pair programmer that costs nothing to use and runs 24/7 on your own terms.

How does a local AI coding setup compare to cloud solutions?

When comparing local and cloud AI coding assistants, the core difference comes down to freedom vs. convenience. A local setup gives you freedom – it’s open-source, modifiable, and independent of any company’s policies – whereas cloud solutions offer convenience and polish at a price.

For example, GitHub Copilot or Cursor provide superb integration with virtually zero setup, but you’re trading away some privacy and paying ongoing fees. With a local stack, you’ve seen that tools like Continue/Cline + Qwen can approach the intelligence of those services.

In fact, recent benchmarks show open models nearly matching proprietary ones for many coding tasks. But there are still cases where something like GPT-4 (via Copilot’s backend) will produce a more accurate or complex solution than a 13B open model could.

One way to look at it: if you already have the hardware (or are willing to invest in it) and you enjoy being on the cutting edge of open-source AI, building your own AI dev assistant is incredibly rewarding. You become part of a movement pushing for AI access without gatekeepers.

On the flip side, if you just want results quickly and don’t mind sending your code to a service, the cloud options might get you slightly better results with zero fuss. Many developers ultimately try both: for example, using Copilot at work (where code is less sensitive or the company provides it) but using a local AI assistant at home for personal projects or highly confidential work.

Next, we’ll provide a detailed side-by-side comparison of local vs cloud AI coding assistants, including specific tools (Copilot, Cursor, Claude, Gemini, etc.), their costs, and which might suit different scenarios. If you’re curious how the open-source Qwen stack stands up against the likes of GPT-4, keep reading!

Local vs Cloud AI Coding Assistants: Complete Comparison Table

Feature	Local AI Assistants	Cloud AI Assistants
Setup Complexity	Requires technical knowledge, Docker/Ollama setup	One-click installation, plug-and-play
Monthly Cost	$0 (after hardware investment)	$10-20/month (Copilot/Cursor)
Upfront Cost	$1000-1500 for high-end GPU	$0 (free tiers available)
Privacy & Security	Code never leaves your machine	Code sent to third-party servers
Model Quality	Good (Qwen3-32B scores 0.8 vs Claude’s 1.0)	Excellent (GPT-4, Claude, Gemini)
Context Window	Up to 32K tokens (Qwen3)	Up to 1M tokens (Gemini)
Offline Capability	Full offline functionality	Requires internet connection
Customization	Full control over models and settings	Limited to provider options
Support	Community forums, self-troubleshooting	Professional support teams
Updates	Manual model updates	Automatic updates
Best For	Privacy-conscious developers, cost-conscious users	Convenience-focused developers, teams

Comparison based on Qwen3 model capabilities and current cloud AI pricing.

Top 10 Open-Source AI Coding Tools (2025)

1. Qwen3-235B-A22B - Best overall open-source coding model (MoE architecture) (Hugging Face)
2. Qwen3-32B - Excellent performance for most coding tasks (Hugging Face)
3. Qwen3-1.7B - Most accessible option for mid-range hardware (Hugging Face)
4. Code Llama - Meta’s solid general-purpose coding model (Hugging Face)
5. WizardCoder - Strong performance on coding benchmarks (Hugging Face)
6. Continue - Best VS Code extension for local AI (GitHub)
7. Cline - Best autonomous coding agent extension (GitHub)
8. Goose - Best external AI agent framework (GitHub)
9. Ollama - Best local model serving solution (ollama.ai)
10. LM Studio - Best GUI for local model management (lmstudio.ai)

Step-by-Step Setup Guide for Local AI Coding Assistant

Step 1: Install VS Code and Extensions

1. Download and install Visual Studio Code
2. Install the Continue extension from the marketplace
3. Install the Cline extension (optional, for agentic features)
4. Install Ollama for local model serving

Step 2: Set Up Ollama and Download Models

1. Download Ollama from ollama.ai
2. Open terminal and run: ollama pull qwen3:1.7b
3. For better performance, try: ollama pull qwen3:32b
4. Test with: ollama run qwen3:1.7b "Write a Python function to calculate fibonacci"

Step 3: Configure Continue Extension

1. Open VS Code settings
2. Search for “Continue”
3. Set the API endpoint to: http://localhost:11434
4. Set the model name to: qwen3:1.7b
5. Test by opening a Python file and using Ctrl+Shift+L

Step 4: Configure Cline (Optional)

1. Open Cline settings in VS Code
2. Set the model provider to “Ollama”
3. Configure the model name and endpoint
4. Test agentic features with a simple command

Step 5: Test Your Setup

1. Create a new Python file
2. Write a comment describing what you want
3. Use Continue to generate code
4. Use Cline to refactor or improve the code
5. Verify everything works offline

Hardware Requirements by Model Size

Model Size	Minimum GPU VRAM	Recommended GPU	RAM	Storage
Qwen3-1.7B	4GB	RTX 3060/4060	8GB	5GB
7B Models	8GB	RTX 3070/4060	16GB	20GB
13B Models	12GB	RTX 3080/4070	24GB	30GB
Qwen3-32B	20GB	RTX 3090/4080	32GB	60GB
Qwen3-235B-A22B	48GB+	Multiple RTX 4090s	128GB+	500GB+

Hardware requirements based on Qwen3 model specifications and community testing.

Troubleshooting Common Issues

Model won’t load: Check GPU VRAM and try a smaller model or quantization.

Slow performance: Reduce model size, use quantization, or upgrade hardware.

Extensions not connecting: Verify Ollama is running and endpoint is correct.

Out of memory errors: Close other applications or use a smaller model.

Poor code quality: Try a larger model or different quantization settings.