Local LLM Coding: Zero-Cost Setup with Ollama & Continue.dev

Build a private AI coding setup with Ollama and Continue.dev in VS Code, including hardware needs, model choices, and realistic quality tradeoffs.

Running a Local LLM for Coding in 2026: Ollama + Continue.dev, Zero API Costs

In an era dominated by cloud-hosted AI, the appeal of a completely local large language model (LLM) setup for coding is stronger than ever. For developers wary of sending proprietary code to third-party servers, or simply those looking to cut API costs, a local LLM offers a compelling alternative. This guide details how to build a robust, zero-cost, privacy-first coding assistant using Ollama as your model runner and Continue.dev as your VS Code frontend. This isn’t about replacing Copilot entirely, but understanding how to leverage local AI where it truly shines.

1. Why Local? The Tradeoffs You’re Making (and Gaining)

The decision to run an LLM locally comes with a distinct set of tradeoffs. Understanding these upfront is crucial for setting realistic expectations.

The Gains:

• Privacy and Security: Your code never leaves your machine. This is paramount for projects involving sensitive data, proprietary algorithms, or companies with strict data residency and compliance policies. For air-gapped environments, local LLMs are the only option.
• Zero API Costs: Once the models are downloaded, there are no ongoing per-token or subscription fees. This makes it ideal for hobbyists, students, or anyone experimenting heavily without worrying about runaway cloud bills.
• Offline Capability: Develop anywhere, even without an internet connection. Your AI assistant is always available.
• Customization: Full control over which models you run, their quantization, and potentially fine-tuning (though beyond the scope of this guide).

The Tradeoffs:

• Hardware Investment: You become responsible for providing the computational horsepower. This means potential upfront costs for capable CPUs, RAM, and especially GPUs.
• Performance: Generally, local models, especially smaller ones, will not match the raw reasoning power, context handling, or speed of the largest, proprietary cloud models (e.g., GPT-4, Claude 3 Opus) running on server-grade hardware.
• Model Size and Capabilities: You are limited by your machine’s resources, meaning you’ll often be running smaller models (e.g., 7B, 14B parameters) compared to the 70B+ models often backing cloud services. This impacts code understanding and generation quality.
• Setup Overhead: It requires a bit more hands-on setup than simply installing a VS Code extension.
• Lack of Internet Context: Local models cannot browse the web for up-to-date documentation, search for error messages, or integrate with live APIs.

Ultimately, a local setup is about control, privacy, and cost-efficiency over raw, bleeding-edge performance.

2. Hardware Requirements: Understanding the Minimums (and Realities)

The most significant hurdle for adopting local LLMs is hardware. While modern CPUs can run small models, a dedicated GPU significantly accelerates inference. Memory (RAM or VRAM) is the critical factor, as the entire model or a large portion of it must reside in memory during inference.

• Minimum 16GB RAM: This is generally sufficient for running 7-billion parameter (7B) models, especially if you have an integrated GPU that shares system RAM (like Apple Silicon Macs).
• Minimum 32GB RAM: Recommended for 14B models, offering a noticeable improvement in model quality and context handling. If you plan to run larger models or frequently switch between them, 64GB is a safer bet.

GPU VRAM Recommendations (for faster inference):

• 10GB VRAM (e.g., RTX 3080, RTX 4060 Ti 16GB): Can comfortably run 7B models at good speeds and often handle 14B models, though some layers might spill into system RAM (CPU offloading), impacting speed.
• 16GB Unified Memory (e.g., M2/M3 MacBook Pro 16GB): Apple Silicon’s unified memory architecture is highly efficient for LLMs. A 16GB M-series chip can run 7B models very well, often outperforming discrete GPUs with similar VRAM on paper due to superior memory bandwidth and integration.
• 24GB+ VRAM (e.g., RTX 3090, RTX 4090): The sweet spot for enthusiast users. This allows you to run 14B models entirely on GPU, and even experiment with larger models (e.g., 34B) with some CPU offloading.

Key takeaway: More RAM/VRAM is always better. If your budget allows, prioritize unified memory on Apple Silicon or higher VRAM on discrete NVIDIA GPUs. AMD GPU support is improving but still less mature than NVIDIA for consumer LLM inference. [VERIFY: Hardware recommendations and software support for GPUs are rapidly evolving.]

3. Choosing Your Model: Performance Tiers for Your Rig

The landscape of open-source LLMs trained for code is rich and constantly expanding. We recommend models specifically designed for coding tasks, which tend to outperform general-purpose models for developer workflows. These models are instruction-tuned, meaning they’ve been trained to follow commands effectively.

• 8GB RAM Tier:

  • qwen2.5-coder:3b: A solid choice for very limited hardware. Despite its small size, it’s explicitly trained on code and can provide decent autocomplete and simple suggestions. Expect basic functionality, good for boilerplate.

• 16GB RAM Tier:

  • qwen2.5-coder:7b: A significant jump from the 3B variant. This model offers much better context understanding and more coherent code generation. It’s a sweet spot for many developers with standard laptops or entry-level discrete GPUs.
  • deepseek-coder-v2:lite: DeepSeek models are renowned for their code capabilities. The lite version (often around 7B-8B parameters) provides excellent code understanding and generation for its size, making it a strong contender for this tier.

• 32GB+ RAM Tier:

  • qwen2.5-coder:14b: For those with ample RAM or higher-VRAM GPUs, the 14B Qwen2.5 Coder offers robust performance, capable of handling more complex functions and larger code blocks.
  • deepseek-coder-v2:16b: The 16B variant of DeepSeek Coder V2 is one of the best performing open-source code models available for this size tier. It excels in diverse programming languages and complex problem-solving within its context window.

Why these models? They are specifically trained on vast datasets of code, making them inherently better at understanding syntax, patterns, and typical developer requests. Their instruction-tuning ensures they respond well to prompts like “explain this function” or “refactor this code.”

A quick note on quantization: Models come in different quantization levels (e.g., Q4_0, Q5_K_M). These refer to how precisely the model’s weights are stored, impacting file size, memory usage, and slightly affecting quality. Ollama usually pulls a balanced default, but you can specify different quantizations (e.g., ollama pull deepseek-coder-v2:lite-q4_0) if you need to squeeze it onto less memory, at the cost of some performance.

4. Ollama Setup: Your Local LLM Server

Ollama is a fantastic tool that simplifies running LLMs locally. It handles the complexities of downloading models, managing dependencies, and exposing a user-friendly API endpoint.

Step 1: Install Ollama Download and install Ollama from their official website: ollama.ai. It’s available for macOS, Linux, and Windows. The installation is straightforward, typically a one-click process that backgrounds the Ollama server for you.

Step 2: Pull a Model Once Ollama is installed, open your terminal or command prompt. We’ll pull one of the recommended models. For this example, let’s use deepseek-coder-v2:lite.

ollama pull deepseek-coder-v2:lite

This command will download the model weights (which can be several gigabytes). Be patient, as this depends on your internet speed.

Step 3: Test Your Model You can interact with your model directly from the terminal to ensure it’s working:

ollama run deepseek-coder-v2:lite
>>> print hello world in python

The model should respond with the Python code. You can also test the API directly using curl:

curl http://localhost:11434/api/generate -d '{
  "model": "deepseek-coder-v2:lite",
  "prompt": "write a python function to add two numbers"
}'

This confirms Ollama is serving the model correctly.

5. Continue.dev Setup: Integrating with VS Code

Continue.dev is a powerful, open-source VS Code extension that brings conversational AI, autocomplete, and agentic workflows directly into your IDE. It’s designed to be model-agnostic, making it perfect for connecting to your local Ollama instance.

Step 1: Install the Continue VS Code Extension Open VS Code, navigate to the Extensions view (Ctrl+Shift+X or Cmd+Shift+X), search for “Continue,” and install it.

Step 2: Configure Continue.dev to use Ollama After installation, you’ll see a new Continue icon in your sidebar. Click it. Continue will prompt you to choose a model. Instead of selecting a cloud provider, we’ll edit its configuration file.

Open the VS Code Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and search for “Continue: View Config”. This will open ~/.continue/config.json (or a similar path on Windows).

Here’s the essential configuration to point Continue.dev to your local Ollama instance:

{
  "models": [
    {
      "name": "deepseek-coder-v2:lite",
      "provider": "ollama",
      "base_url": "http://localhost:11434",
      "description": "My local DeepSeek Coder v2 Lite model via Ollama"
    },
    // You can add other Ollama models here if you pull them
    {
      "name": "qwen2.5-coder:7b",
      "provider": "ollama",
      "base_url": "http://localhost:11434",
      "description": "My local Qwen2.5 Coder 7B model via Ollama"
    }
  ],
  "defaultModel": "deepseek-coder-v2:lite", // Set your preferred default model
  "tabAutocompleteModel": {
    "name": "deepseek-coder-v2:lite", // Use the same model for autocomplete
    "provider": "ollama",
    "base_url": "http://localhost:11434"
  },
  "enableTabAutocomplete": true, // Ensure autocomplete is enabled
  "slashCommands": [
    {
      "name": "chat",
      "description": "Chat with the LLM directly.",
      "prompt": "You are a helpful programming assistant. Respond to the user's query.\n\n{{PROMPT}}",
      "model": {
        "name": "deepseek-coder-v2:lite"
      }
    },
    {
      "name": "edit",
      "description": "Apply edits to the current file.",
      "prompt": "The user wants to edit the current file. Read the context and the provided prompt to determine what changes should be made. Respond with only the changed code, including indentation, and no explanations. Do not include any unchanged code.\n\n{{PROMPT}}",
      "model": {
        "name": "deepseek-coder-v2:lite"
      }
    }
  ],
  "requestOptions": {
    // Optional: Adjust request timeout if you have slower inference
    "timeout": 60000 // 60 seconds
  }
}

Key points in the config:

• models array: Defines your available local models. Ensure the name matches the model name you pulled with Ollama, and provider is ollama. base_url points to Ollama’s default API endpoint.
• defaultModel: Sets which model Continue.dev uses by default for chat.
• tabAutocompleteModel: Crucially, this tells Continue.dev to use your local Ollama model for inline code suggestions.
• enableTabAutocomplete: Must be true to get those helpful suggestions.
• slashCommands: Configure commands like /chat and /edit to use your local model. You can customize the system prompts here.
• requestOptions.timeout: If your local machine is slow, you might need to increase this timeout to prevent Continue.dev from giving up before the model finishes generating.

Save the config.json file. Continue.dev should automatically detect the changes. You might need to reload VS Code if it doesn’t.

6. Daily Workflow & Realistic Expectations

Now that your local setup is complete, it’s time to put it to work. Understanding its capabilities and limitations is key to a productive workflow.

• Autocomplete (Inline Suggestions):

  • Quality: Expect around 70-80% the quality of cloud services like GitHub Copilot or Cursor for common coding patterns. This means variable names, simple function completions, and basic loop structures will often be accurate.
  • Limitations: It struggles significantly with project-specific context, understanding complex API usage unique to your codebase, or generating highly creative solutions. Without internet access, it cannot pull in external library documentation.
  • Performance: After an initial “cold start” (when the model is loaded into VRAM/RAM), subsequent suggestions are generally fast.

• Chat Mode:

  • Use Cases: Excellent for explaining unfamiliar code snippets, refactoring small functions, generating boilerplate code (e.g., “write a Python class for a linked list”), or debugging simple errors where the context fits within the model’s window.
  • Limitations: While helpful, it won’t perform complex, multi-step reasoning or act as a true “rubber duck debugging” partner for deep architectural problems. Its knowledge is frozen at its training cutoff.

• Agent Mode (e.g., /edit command):

  • Quality: Compared to advanced cloud-based agents (e.g., Cursor’s agent, GPT-4 with browsing), local agent mode is significantly weaker. It’s limited by the context window of your local model and its reasoning capabilities.
  • Use Cases: Best for highly scoped tasks, like “add docstrings to this function,” “convert this function to an async version,” or “rename this variable consistently within this file.”
  • Limitations: It will struggle with tasks requiring extensive planning, multiple file modifications, or integrating with external tools (unless explicitly configured with local tools, which is an advanced topic). Don’t expect it to fix a complex bug across your entire codebase.

• Context Limits: Even if a local model claims a 128k token context window, the practical usable context often feels smaller due to its reasoning capacity. Feeding a smaller model a huge context window doesn’t magically make it smarter; it often dilutes its focus. Be mindful of the active file and surrounding code.

7. When Local LLMs Excel (and When They Don’t)

Knowing when to use your local LLM and when to reach for a cloud service is the mark of an effective developer.

Where Local LLMs Win:

• Air-Gapped Environments: Absolutely no internet access? No problem. Local LLMs are indispensable here.
• Strict Data Privacy: Working with highly confidential or proprietary code where data exfiltration is a non-starter. Compliance requirements often mandate local processing.
• Learning and Experimentation: For students and hobbyists, the zero-cost model is a game-changer. Experiment freely without incurring surprise bills.
• Repetitive, Boilerplate Tasks: Generating common code structures, simple tests, or converting code between similar syntaxes.
• Explaining Local Code: Asking your LLM to explain a complex function or a section of code that you’re reviewing.

Where Local LLMs Don’t Win (or struggle significantly):

• Complex Problem Solving: Tasks requiring deep reasoning, novel solutions, or integrating knowledge from current web sources.
• Large-Scale Refactoring: Modifying code across many files, understanding intricate project architecture, or making decisions based on live system behavior.
• Cutting-Edge Research/Features: Cloud models are often at the forefront of new capabilities (e.g., multimodal inputs, longer contexts, better tool integration).
• Users with Insufficient Hardware: If your machine can barely run a 3B model, your experience will be frustratingly slow and low-quality.

8. Gotchas and Troubleshooting

Even with a streamlined setup, you might encounter some bumps.

• Cold Start Time: The very first request after the model has been idle (or after restarting Ollama) will be slow. This is because the model weights need to be loaded from disk into your GPU’s VRAM or system RAM. Subsequent requests will be much faster. Be patient.
• Quantization Artifacts / Hallucinations: Smaller or more aggressively quantized models can sometimes produce less coherent code or “hallucinate” incorrect syntax or non-existent functions, especially with complex prompts. If this happens, try a slightly larger model or one with higher quantization (e.g., Q5_K_M instead of Q4_0).
• Keeping Models Updated: Open-source models are frequently updated. To get the latest version (which often includes bug fixes and better performance), run ollama pull <model_name> periodically.
• Resource Contention: If you’re running other memory-intensive applications or games, your LLM’s performance will suffer, as it competes for RAM/VRAM.
• Disk Space: Models can be large (3GB-30GB+ per model). Ensure you have enough disk space if you plan to try multiple models.
• Ollama Server Not Running: If Continue.dev can’t connect, double-check that the Ollama server process is active. On macOS and Windows, it usually runs in the background. On Linux, you might need to manually start it with ollama serve.

9. Next Steps: Local Agents (Optional but Powerful)

While Continue.dev provides a good agentic framework with its /edit and custom slash commands, you can push the boundaries of local AI further. Tools like Claude Code or Cline (open-source projects often built on top of Ollama) aim to create fully local, autonomous coding agents.

These frameworks allow you to:

• Define custom tool sets (e.g., read file, write file, execute shell command, run unit tests).
• Give the LLM more complex, multi-step objectives (e.g., “implement a new feature according to this spec,” “find and fix all performance bottlenecks in this module”).
• Orchestrate interactions between the LLM and your codebase more dynamically.

Integrating Ollama with these more advanced agent frameworks extends its utility beyond simple chat and autocomplete, transforming your local LLM into a more proactive coding assistant. This path requires more configuration and scripting but unlocks significant potential for fully autonomous local development workflows.

Conclusion

Setting up a local LLM for coding with Ollama and Continue.dev in 2026 is a practical, powerful endeavor. It won’t replace the cutting-edge capabilities of cloud LLMs overnight, but it offers unparalleled privacy, cost control, and offline access that are crucial for specific use cases. By understanding the hardware requirements, selecting appropriate models, and managing expectations, developers can build a robust, personal AI coding assistant tailored to their needs. This setup empowers you with more control over your tooling, ensuring your code remains yours, while still benefiting from the transformative power of AI.