An average development team using cloud-hosted AI APIs spends between $30,000 and $150,000 annually on tokens alone. Add data governance risk, compliance overhead, and the latency that compounds when your AI calls have to round-trip through an external server, and it becomes a serious tax on your infrastructure budget.
Local LLMs eliminate all three problems simultaneously. You run the model on your own hardware. Data stays inside your perimeter. Your engineers ship faster.
What you're actually choosing when you go local is bigger than cost reduction. You're making a structural decision about where your intelligence layer lives. Local LLMs mean your model has zero visibility into your proprietary codebase. Performance stays stable regardless of silent upstream model updates. Your engineering environment becomes owned end to end, with no third-party dependencies inside your development loop.
This guide shows the exact setup we use at Energma for a fully private AI development environment that costs nothing to run after setup and works entirely offline.
Tools & Download Links:
Install once. Run any open-source LLM on your own hardware - no cloud required. Ollama acts as a package manager for large language models. It simplifies downloading models from its library, running them locally on your CPU or GPU (minimum 8 GB RAM), and serving them through a clean local API.
Run the following command to install Ollama:
1curl -fsSL https://ollama.com/install.sh | shAfter installation, verify that Ollama is installed correctly:
1ollama --versionIf installed successfully, the command will return the installed version number.
Next, choose an LLM compatible with Ollama from its model library. For purposes of this guide we are going to use lightweight Qwen3:8b version which is downloadable here.qwen3:8b
Download the model by running:
1ollama pull qwen3:8bTo confirm the model is available locally, run:
1ollama listThe output should look similar to:
1NAME ID SIZE MODIFIED2qwen3:8b xxxxxxxxxxxx 5.2 GB 5 minutes agoOpenCode is an AI-powered coding assistant that runs directly in your terminal. It works like a pair programmer, understanding your entire codebase to generate code, explain existing logic, refactor cleanly, and answer project-specific questions. It uses local LLMs, meaning no internet connection is required once set up.
Run the following command to install OpenCode:
1curl -fsSL https://opencode.ai/install | bash1opencode --versionThe version number will be displayed if installation was successful.
In our local project inside a root directory add .opencode folder.
Now we need to create configuration file for OpenCode that tells it how to connect to a locally running LLM through Ollama.
opencode.json example:
1{2 "$schema": "https://opencode.ai/config.json",3 "provider": {4 "ollama": {5 "npm": "@ai-sdk/openai-compatible",6 "name": "Ollama (local)",7 "options": {8 "baseURL": "http://localhost:11434/v1"9 },10 "models": {11 "qwen3:8b": {12 "name": "qwen3:8b"13 }14 }15 }16 }17}Note: Ollama is running on localhost:11434 by default.
In local project directory inside terminal type opencode
This will open up opencode prompt interface.
In the OpenCode interface, type /connect. This opens the provider selection modal. Type ollama and choose Ollama (local) provider
It will ask you to enter API Key. Since Ollama runs locally, no real API key is required.
Type ollama-local as an API key.
Select LLM model from interface, choose our qwen3:8b like so:
After those steps you will be returned to prompt interface where you can see default language model is now changed to qwen3:8b.
The setup is now complete and the stack is running.
Local LLMs are one piece of a broader shift in how high-performance engineering teams are architecting their AI stack. The engineers who thrive in the next three years won't be the ones who use AI the most. They'll be the ones who understand where to put it, how to control it, and how to build systems around it that don't create new dependencies and technical debt.
If your current AI tooling creates data risk, unpredictable costs, or latency you can't control - the problem is already costing you. The question isn't whether to fix it. The question is whether you fix it with a setup guide or with a proper architectural review. That's the conversation we have with engineering leaders every week.[Book yours here →]