I've been experimenting with building a genuinely private local AI coding assistant. The discussions lately, especially around the soaring costs of API calls and increasing privacy concerns like those highlighted in the "AI is Getting More Expensive , We Have the Fix" video, got me thinking. Why send my code to some distant server when I could keep it all on my machine?
The NVIDIA Developer channel recently showcased "Private, Local AI CUDA Coding Assistance on DGX Spark," which is cool if you have a DGX Spark laying around. But what about us developers with more modest hardware? Can we get a similar experience without breaking the bank or compromising our data?
Why Go Local and Private for AI Coding?
Honestly, the push for local AI is getting stronger. For one, cost. Services like GitHub Copilot or even Claude Code are fantastic, but those subscriptions add up. And the API costs for more advanced models can get wild if you are doing a lot of iteration. This is a core reason I have been exploring options on our Local AI Guide. It is about control and cost savings, something I dug into deeper in How to bypass AI API costs with local models in 2026.
Then there is privacy. When you send your code to a cloud service, you are trusting them with your intellectual property. For sensitive projects, that is just not an option. A private local AI setup means your code never leaves your machine. This is particularly important for enterprise teams, as we discussed in Why Enterprise Teams Need Local AI Coding in 2026.
My Experiment: Setting up a Private Local AI Coding Assistant
My goal was straightforward: get a decent open source code completion and assistance model running on my local machine, accessible from my text editor. No cloud APIs, no data sharing. I wanted to see if the quality was good enough to be genuinely useful.
I considered several options. LM Studio is popular for its GUI, but I prefer the command line for this kind of thing. Ollama has been gaining a lot of traction for its ease of use with open source models. Given its OpenAI compatible API, it felt like the right choice to integrate with existing editor extensions.
For the model itself, I looked at a few. CodeLlama is a solid choice, but I've had some good experiences with DeepSeek models recently. The DeepSeek V4 models, and specifically the DeepSeek Coder 33B Instruct, seemed promising for coding tasks. It is available quantized, which is key for local runs.
Step by Step: Installing Ollama and a Code Model
I started with a fresh install of Ollama on my workstation running Ubuntu 22.04 with an NVIDIA RTX 3090. The installation is pretty simple:
curl fsSL https://ollama.com/install.sh | sh
This script handles everything, setting up the service and making the ollama command available. After that, I grabbed the deepseek coder model:
ollama pull deepseek coder
This pulls the default tag for deepseek coder, which at the time of writing was deepseek coder:33b instruct q4_K_M. It is a 4 bit quantized version of the 33 billion parameter instruct model. It took a few minutes to download the 20GB file, but once it was done, I could run it directly:
ollama run deepseek coder
I immediately got a prompt in my terminal. I typed in a simple request:
>>> Write a Python function to calculate the Nth Fibonacci number recursively.
And it responded with:
def fibonacci_recursive(n): if n <= 0: return 0 elif n == 1: return 1 else: return fibonacci_recursive(n 1) + fibonacci_recursive(n 2)
It was quick, maybe a second or two for that simple function. Not bad for running entirely on my GPU.
Integrating with an Editor: VS Code
A terminal is great for quick tests, but I wanted this in my actual coding workflow. I use VS Code heavily. The key here is that Ollama exposes an OpenAI compatible API on http://localhost:11434/v1/. This means many existing AI coding extensions designed for OpenAI's API can be reconfigured to point to Ollama.
I installed the "CodeGPT" extension (version 2.8.12) in VS Code. Once installed, I opened my VS Code settings (Cmd+, or Ctrl+,) and searched for "CodeGPT". I configured it like this:
// settings.json
{ "codegpt.apiKey": "ollama", // Can be any string, not actually used by Ollama "codegpt.baseUrl": "http://localhost:11434/v1", "codegpt.model": "deepseek coder", "codegpt.language": "Python"
}
Restarting VS Code (important for some extensions to pick up new settings) and then opening a Python file. I highlighted a piece of code and used the "Ask CodeGPT" command. It worked! Completions were appearing right in my editor.
Testing the Setup: A Real World Code Example
I decided to test it with a slightly more complex task: refactoring a synchronous file processing function into an asynchronous one using asyncio.
I started with this Python code:
import os def process_file(filepath): with open(filepath, 'r') as f: content = f.read() # Simulate some processing processed_content = content.upper() return processed_content def process_directory(directory_path): results = {} for filename in os.listdir(directory_path): filepath = os.path.join(directory_path, filename) if os.path.isfile(filepath): results[filename] = process_file(filepath) return results # Example usage:
# dir_path = './my_data'
# processed_data = process_directory(dir_path)
# print(processed_data)
I highlighted the entire process_directory and process_file functions and prompted CodeGPT with "Refactor this to use asyncio for concurrent file processing."
The response was impressive. It provided a complete refactoring, including async and await keywords, asyncio.to_thread for file I/O, and an asyncio.gather call. The latency for this more involved request was about 5 7 seconds, which is totally acceptable for an assistance tool running on my hardware.
TIL: Prompting for specific library usage (e.g., "using asyncio") dramatically improves the output quality for specialized tasks with these open source models. They are less forgiving than something like GPT 5.5 or Gemini 3.1 Ultra if you are vague.
Dead Ends and Learnings
My first attempt was with a smaller 7B parameter model. While it was faster, the code quality and understanding were noticeably worse. It often missed critical details or produced boilerplate that needed heavy editing. The 33B deepseek coder model was the sweet spot for my 24GB VRAM GPU. Anything larger would have pushed it into CPU RAM, making it too slow to be practical.
Another learning: make sure your model's context window is sufficient for your code base. For full file or project context, you need models with larger context windows. The deepseek coder has a decent context, but for very large files, I still found myself breaking down prompts.
Here's the interesting part: The Privacy and Cost Advantage
This setup allows me to get genuinely useful coding assistance without any external network calls. My code stays on my machine, private and secure. And the cost? Zero, beyond the electricity my GPU uses. Compare that to the monthly fees for tools like GitHub Copilot ($10/month) or Cursor Editor (which offers a freemium tier but costs for advanced features). While these cloud tools offer some conveniences, the privacy and cost benefits of a local setup are significant, especially as API costs continue to rise. You can even Compare GitHub Copilot vs Cursor Editor on our site to see the pricing differences.
Beyond Basic Completion: Local Agents?
The YouTube trends also highlighted projects like "nanobot: Local AI Agent" and "How to Build an Autonomous Local AI Agent (Hermes)". This is the next frontier. While my setup is primarily for code completion and focused assistance, the Ollama API provides a foundation for building more autonomous local agents. You could chain prompts, use local RAG (Retrieval Augmented Generation) for codebase awareness, and even integrate with local testing frameworks, all without touching the cloud.
I'm excited about the possibilities this opens up. The barrier to entry for serious local AI development is dropping rapidly, and with tools like Ollama and powerful open source models like DeepSeek V4, anyone can set up a private, powerful AI coding assistant.
You can try this yourself. All you need is a machine with a decent GPU (at least 16GB VRAM for a good experience with a 33B model, though smaller models work with less), Ollama, and a compatible VS Code extension. The freedom and privacy you gain are well worth the initial setup. Explore more options on our browse 600+ AI tools page and use our track your AI spend feature to see how much you save.
FAQ
Can I run this setup on a Mac or Windows?
Yes, Ollama provides installers for macOS and Windows, in addition to Linux. The process for pulling and running models, and configuring VS Code, remains largely the same across platforms.
What are the minimum hardware requirements for a local AI coding assistant?
A minimum of 8GB of RAM is generally recommended, but for decent performance with a larger model like DeepSeek Coder 33B, you'll ideally want a GPU with at least 16GB VRAM (24GB for optimal performance). Smaller quantized models (e.g., 7B or 13B parameters) can run on less, even CPU only, but with reduced quality and speed.
Is a local AI coding assistant as good as cloud based options like GitHub Copilot?
For raw completion quality and breadth of knowledge, dedicated cloud services often still have an edge due to their access to larger, proprietary models. However, for privacy, cost control, and customization, a local setup is often superior. For common coding tasks, a well chosen open source model like DeepSeek Coder is surprisingly effective and continually improving.
