Running Starcoder2 Locally for Code Completion Without

Starcoder2 running locally is the solution for developers who need AI code completion while keeping sensitive code private. You can install it using Ollama (install Ollama, run ollama pull starcoder2:7b, then integrate it with VS Code via the Continue extension) and have your code stay completely on your local machine without any cloud transmission. This setup takes about 30 minutes and requires a machine with 16GB+ RAM and optional GPU for faster inference.

Understanding Starcoder2 and Local Code Completion

Starcoder2 is a family of open-source code generation models developed by BigCode, designed specifically for code completion and generation tasks. These models are trained on a diverse corpus of programming languages and can generate contextually appropriate code suggestions in real-time.

The key advantage of running Starcoder2 locally is privacy. When you use cloud-based alternatives, your code gets transmitted to external servers for processing. For developers working with sensitive codebases, regulated industries, or organizations with strict data governance policies, this transmission creates compliance challenges. By running the model locally, you maintain full control over your intellectual property.

Prerequisites for Running Starcoder2 Locally

Before setting up Starcoder2 for local code completion, ensure your system meets the basic requirements. You’ll need a machine with at least 16GB of RAM for smaller variants like Starcoder2-3b, though the 7b and 15b models require more memory. A dedicated GPU significantly improves inference speed, but CPU-only inference remains viable for basic code completion tasks.

The setup process involves installing Ollama, a runtime that makes running large language models locally straightforward. Ollama supports various models including Starcoder2 variants and provides a simple API for integrating with code editors.

Setting Up Ollama and Starcoder2

The installation process begins with setting up Ollama on your system. On macOS, you can install it via Homebrew:

brew install ollama

For Linux and Windows (via WSL), use the installation script:

curl -fsSL https://ollama.com/install.sh | sh

After installing Ollama, pull the Starcoder2 model of your choice. The 7b model offers a good balance between performance and resource usage:

ollama pull starcoder2:7b

For systems with more resources, the 15b model provides more accurate suggestions:

ollama pull starcoder2:15b

Verify the installation by running a simple test:

ollama run starcoder2:7b "def fibonacci(n):"

This command sends a prompt to the local model and returns the generated completion.

Integrating Starcoder2 with VS Code

To use Starcoder2 for code completion in Visual Studio Code, you have several integration options. The most straightforward approach uses the Continue extension, which provides AI assistance directly within VS Code.

Install the Continue extension from the VS Code marketplace, then configure it to use your local Ollama instance. Open the extension settings and specify the local model endpoint:

{
  "continue.backend": "ollama",
  "continue.model": "starcoder2:7b",
  "continue.ollamaUrl": "http://localhost:11434"
}

After configuration, the extension will use your local Starcoder2 model for code suggestions instead of sending requests to cloud services.

Alternative Integration with Neovim

Neovim users can integrate Starcoder2 using the CodeLLM plugin or by configuring the Ollama API directly with tools likenvim-llama. Another popular option combines Ollama with the custom completion framework of your choice.

For basic integration using Ollama’s API, create a simple function in your Neovim configuration:

local function get_completion(prompt)
  local http = require("socket.http")
  local json = require("cjson")

  local response = {}
  local body = json.encode({
    model = "starcoder2:7b",
    prompt = prompt,
    stream = false
  })

  local res, code = http.request{
    url = "http://localhost:11434/api/generate",
    method = "POST",
    headers = {
      ["Content-Type"] = "application/json",
      ["Content-Length"] = #body
    },
    source = ltn12.source.string(body),
    sink = ltn12.sink.table(response)
  }

  local result = json.decode(table.concat(response))
  return result.response
end

This function sends code context to your local Ollama instance and returns the completion.

Optimizing Performance for Local Inference

Running code completion locally requires understanding how to optimize inference for your specific hardware. The primary considerations are memory availability, response latency, and suggestion quality.

For GPU acceleration, ensure CUDA is available if you’re using an NVIDIA card:

export CUDA_VISIBLE_DEVICES=0
ollama run starcoder2:7b

To improve response times, keep the model loaded in memory rather than starting a new process for each completion:

ollama serve
# Keep this process running

In your editor configuration, adjust timeout settings to account for local inference time. A 2-3 second timeout is reasonable for CPU-only inference with the 7b model.

Comparing Starcoder2 Variants

Starcoder2 comes in three primary sizes, each suited to different use cases:

Model

Parameters

RAM Required

Best For

|——-|————|————–|———-|

Starcoder2-3b

6GB

Quick suggestions, older hardware

Starcoder2-7b

14GB

Balanced performance

Starcoder2-15b

15B

30GB

Complex code generation

The smaller 3b model works well for basic completion tasks and runs smoothly on laptops without dedicated GPUs. The 7b model handles most development scenarios effectively, while the 15b variant excels at understanding complex codebases but requires significant resources.

Troubleshooting Common Issues

Several common issues arise when setting up local code completion. If the model fails to load, check that you have sufficient available memory:

# Check available memory on macOS
vm_stat

# Check on Linux
free -h

For connection errors between your editor and Ollama, verify the service is running:

ollama list
ps aux | grep ollama

If suggestions seem poor quality, try providing more context in your prompts. Starcoder2 performs better when it has surrounding code to understand the context.

When Local Code Completion Makes Sense

Local code completion using Starcoder2 works particularly well in specific scenarios. Developers working with proprietary code that cannot leave the organization benefit most from this approach. Similarly, those in industries with strict compliance requirements, such as healthcare or finance, often must keep all code within their own infrastructure.

Developers in regions with limited internet connectivity or those who travel frequently find local models invaluable. The consistent availability of code completion regardless of network conditions improves productivity significantly.

However, cloud-based solutions may still be preferable when you need the most advanced suggestions, have unlimited internet access, and don’t have stringent privacy requirements. Cloud models like GPT-4 or Claude generally provide more accurate and contextually aware suggestions due to their larger training datasets and more extensive compute resources.

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