Comparing Cloud-Based vs. Self-Hosted AI Assistants
If you are a developer, you have likely felt the magic—and the price tag—of GitHub Copilot or Cursor. These tools redefined coding by slashing the time spent hunting through Stack Overflow. However, they come with two nagging problems: a $100+ annual bill and a lack of data privacy. Many teams cannot risk sending proprietary logic to a third-party server, no matter how helpful the AI might be.
Local Large Language Models (LLMs) used to be a hobbyist’s struggle. They were slow, clunky, and often hallucinated more than they helped. That changed recently. With the release of DeepSeek-Coder-V2 and the speed of the Ollama engine, you can now get 50+ tokens per second on a standard laptop. While GPT-4o still wins on complex architectural planning, local models now handle roughly 90% of daily tasks like unit testing and refactoring with identical precision.
This guide pairs Ollama as your local model engine with Continue.dev, an open-source IDE extension. Together, they create a workflow that mimics Copilot’s best features—chat, code editing, and tab-autocomplete—without the monthly invoice.
The Reality of Going Local
Switching to a local setup isn’t just about saving money. It changes your relationship with your tools.
The Advantages
- Zero Latency & Costs: You aren’t paying for tokens or subscriptions. Once the model is on your disk, it is yours to use forever.
- Strict Data Sovereignty: Your code stays on your silicon. This makes local AI a requirement for developers in fintech, healthcare, or defense.
- Offline Freedom: Your assistant doesn’t die when the Wi-Fi does. It works perfectly at 30,000 feet or in a remote cabin.
- Model Swapping: You can use a 1.3B parameter model for lightning-fast autocomplete and switch to a 16B model for deep debugging in seconds.
The Disadvantages
- Hardware Tax: LLMs are hungry. You will need a modern machine—ideally an Apple Silicon Mac (M1/M2/M3) or a PC with a dedicated NVIDIA GPU (RTX 3060 or better).
- Battery Impact: Your cooling fans will spin up. Expect your laptop battery to drain 30-50% faster when the local LLM is active.
- Intelligence Ceiling: Local models are incredible at syntax but can still struggle with high-level abstract logic compared to Claude 3.5 Sonnet.
Hardware Specs and Model Selection
To avoid a sluggish experience, your hardware must fit the model’s weight. I have tested these configurations across various environments to find the sweet spots for performance.
Recommended Specs
- The Entry Point (16GB RAM): Best for 7B or 8B parameter models. This is enough for solid chat and basic completion.
- The Pro Setup (32GB+ RAM): Necessary for DeepSeek-Coder-V2 Lite. This allows the model to stay in memory without slowing down your IDE.
- Disk Space: Reserve at least 30GB. High-quality models typically range from 5GB to 12GB each.
The Best Models for Coding
Not all models speak Python or Rust fluently. These are currently the top performers in the Ollama library:
- DeepSeek-Coder-V2: The current champion. It is a Mixture-of-Experts (MoE) model that rivals GPT-4 in coding benchmarks while remaining small enough to run on consumer hardware.
- Llama 3 (8B): Your best choice for general explanations, documentation writing, and chat.
- DeepSeek-Coder (1.3B): Small but mighty. Use this specifically for tab-autocomplete because it is nearly instantaneous.
Step-by-Step Implementation
Setting this up takes roughly 15 minutes. We will configure the engine first, then the interface.
Step 1: Install Ollama
Ollama acts as the bridge between the model files and your computer’s hardware. Download it from ollama.com. Once installed, it runs as a background service.
Open your terminal and pull the latest coding model:
bash
ollama run deepseek-coder-v2:16b-lite-instruct-q4_K_M
Pro tip: If your machine has less than 16GB of RAM, use ollama run deepseek-coder:6.7b instead to keep things snappy.
Ollama now hosts a local API server at http://localhost:11434. You don’t need to touch this, but it’s what Continue will use to talk to the model.
Step 2: Add the Continue Extension
Open VS Code or your JetBrains IDE of choice. Search the marketplace for “Continue” and install it. A small logo will appear in your sidebar—this is your new command center.
Step 3: Connect the Two
Continue uses a config.json file to point toward your models. Click the gear icon at the bottom of the Continue sidebar. We want to use a heavy model for chat and a light model for autocomplete.
Update your configuration to look like this:
json
{
"models": [
{
"title": "DeepSeek Chat",
"provider": "ollama",
"model": "deepseek-coder-v2:16b-lite-instruct-q4_K_M"
}
],
"tabAutocompleteModel": {
"title": "Fast Autocomplete",
"provider": "ollama",
"model": "deepseek-coder:1.3b-base"
}
}
Using the 1.3B model for autocomplete ensures you don’t feel a “lag” while typing, which is a common complaint with heavier local setups.
Step 4: Testing Your New Assistant
Put your new setup to work immediately with these shortcuts:
- The “Explain” Shortcut: Highlight any confusing function and hit
Cmd/Ctrl + L. Ask “What is this doing?” and watch the response stream in. - The “Refactor” Shortcut: Hit
Cmd/Ctrl + Ion a selected block and type “Rewrite this using async/await.” The AI will show a diff that you can accept or reject. - Ghost Text: As you type, gray suggestions will appear. Hit
Tabto complete the line.
Step 5: Fine-Tuning Performance
If the AI feels sluggish, check your quantization. Models ending in -q4_K_M offer a great balance of speed and intelligence. If you are still seeing slow responses, try a -q2_K version—it uses significantly less VRAM at a slight cost to logic accuracy. Also, remember to close memory-heavy apps like Discord or Chrome tabs when running 16B models on a 16GB machine.
You now have a professional-grade, private coding assistant. This isn’t just a workaround; it’s a superior way to work if you value privacy and want to avoid the constant drip of subscription fees.
