# How to Set Up Self-Hosted AI Code Completion Alternatives to Cursor

> Set up self-hosted AI code completion alternatives to Cursor. Deploy an open-source LLM locally via Ollama or vLLM for private offline code completion in your IDE.

- Repository: [Elliot Chen/one-person-company](https://github.com/cyfyifanchen/one-person-company)
- Tags: how-to-guide
- Published: 2026-02-28

---

**You can replace Cursor with a fully self-hosted stack by deploying an open-source code LLM via Ollama or vLLM, exposing an OpenAI-compatible API endpoint, and redirecting your IDE extension to `http://localhost:8000/v1` for private, offline code completion.**

The cyfyifanchen/one-person-company repository lists Cursor as a top-rated AI-assisted IDE at line 332 of [`README.md`](https://github.com/cyfyifanchen/one-person-company/blob/main/README.md) and defines its external link at line 561, establishing it as the benchmark for AI code completion. While Cursor offers a managed cloud experience, self-hosted alternatives give you complete data sovereignty and eliminate per-token pricing. This guide shows you how to build a local replacement using the same open-source models that power commercial tools.

## Why Choose Self-Hosted AI Code Completion?

**Data privacy** ensures your proprietary source code never leaves your network or passes through third-party servers. **Cost control** shifts you from subscription or per-token pricing to a one-time hardware investment, allowing unlimited completions without rate limits. **Customization** lets you swap models, fine-tune on private codebases, or modify prompt templates for domain-specific languages—flexibility that cloud IDEs rarely expose.

## Architecture of a Cursor Alternative

A self-hosted replacement consists of three layers that mirror Cursor’s backend stack while running entirely on your hardware.

### The Model Layer

You need an open-source code-completion LLM optimized for infilling and function generation. Suitable candidates include Meta’s **CodeLlama** (7B–70B), **StarCoder** (15B), or **DeepSeek-Coder** (33B). For local inference on consumer hardware, download quantized GGUF formats (4-bit precision) that reduce VRAM requirements by 75% while maintaining functional accuracy.

### The Inference Server

This containerized engine exposes an HTTP API compatible with OpenAI’s `/v1/completions` endpoint. **Ollama** provides a single-binary deployment with automatic GPU/CPU fallback and model caching. **vLLM** offers higher throughput for concurrent users via PagedAttention and continuous batching. **text-generation-webui** supplies a Gradio interface alongside its API for debugging prompts.

### Editor Integration Layer

Modern IDEs connect via standard HTTP or Language Server Protocol (LSP). VS Code extensions like ChatGPT or Continue can target your local endpoint by changing the `apiBaseUrl` setting. Alternatively, **Tabby** provides a dedicated self-hosted LSP server that delivers inline ghost text completions without requiring a browser-based interface.

## Step-by-Step Setup Guide

Follow these steps to replicate Cursor’s functionality using Ollama and a lightweight FastAPI proxy.

### 1. Download a Quantized Model

Create a local directory and fetch a 4-bit quantized CodeLlama model optimized for CPU/GPU hybrid inference.

```bash
mkdir -p models && cd models
wget https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/resolve/main/codellama-13b-instruct.Q4_K_M.gguf

```

### 2. Launch the Inference Server

Install Ollama, start the daemon, and pull the model. This automatically exposes a local API on port `11434`.

```bash
curl -fsSL https://ollama.com/install.sh | sh
ollama serve &
ollama pull codellama:13b-instruct-q4_k_m

```

### 3. Expose an OpenAI-Compatible Endpoint

Ollama uses a proprietary `/api/generate` route. Wrap it with a FastAPI proxy to translate requests to OpenAI’s format, allowing standard IDE extensions to connect without modification.

```python

# proxy.py

from fastapi import FastAPI, Request
import httpx

app = FastAPI()

@app.post("/v1/completions")
async def completions(req: Request):
    payload = await req.json()
    async with httpx.AsyncClient() as client:
        resp = await client.post(
            "http://127.0.0.1:11434/api/generate",
            json=payload
        )
    return resp.json()

```

Start the proxy server:

```bash
pip install fastapi uvicorn httpx
uvicorn proxy:app --host 0.0.0.0 --port 8000

```

### 4. Configure Your IDE

In VS Code, install the ChatGPT extension (or any OpenAI-compatible alternative) and modify [`settings.json`](https://github.com/cyfyifanchen/one-person-company/blob/main/settings.json) to point to your local stack.

```json
{
  "chatgpt.apiBaseUrl": "http://localhost:8000/v1",
  "chatgpt.model": "codellama:13b-instruct-q4_k_m",
  "chatgpt.maxTokens": 256,
  "chatgpt.temperature": 0.1
}

```

### 5. Optional Tabby LSP Integration

For native inline completions without a chat interface, deploy Tabby’s containerized LSP server and configure your editor to use WebSocket connections.

```bash
docker run -p 5000:5000 ghcr.io/tabbyml/tabby:latest

```

Set your editor’s LSP client to `ws://localhost:5000` to receive ghost-text suggestions as you type.

## Production-Grade Optimization Tips

**Enable GPU acceleration** by passing `--gpu` to Ollama or launching vLLM with `--tensor-parallel-size` to split models across multiple CUDA devices. **Secure the endpoint** by binding the API to `127.0.0.1` only, or place it behind an NGINX reverse proxy with TLS client certificates and basic authentication. **Optimize prompts** by wrapping your completion context with FIM (Fill-In-Middle) tokens like `<｜fim▁begin｜>` and `<｜fim▁end｜>`, which CodeLlama and StarCoder recognize to generate mid-line insertions rather than just postfix continuations. **Implement caching** with Redis in front of your inference server to serve repeated completion requests instantly without reloading the model.

## Self-Hosted Stack vs. Cursor Comparison

| Feature | Cursor (Cloud) | Self-Hosted (Ollama + CodeLlama) |
|---------|----------------|----------------------------------|
| **Latency** | Network-dependent, 500ms–2s | Sub-500ms on local GPU, near-instant on CPU |
| **Data Privacy** | Code transmitted to Cursor servers | All data remains on localhost |
| **Cost Model** | Free tier limited, $20/month Pro | One-time hardware cost, zero per-token fees |
| **Model Flexibility** | Fixed proprietary model | Swap any GGUF or Safetensors model instantly |
| **Offline Access** | Requires internet connection | Fully functional without external network |

## Summary

- The cyfyifanchen/one-person-company repository positions Cursor as a reference implementation for AI code completion at [`README.md`](https://github.com/cyfyifanchen/one-person-company/blob/main/README.md) lines 332 and 561, providing the baseline for this alternative architecture.
- A complete self-hosted replacement requires three components: a quantized open-source LLM (CodeLlama, StarCoder), an inference server (Ollama, vLLM), and an IDE configured to use a local OpenAI-compatible endpoint.
- Running `ollama serve` and wrapping it with a FastAPI proxy on port `8000` creates a drop-in replacement for Cursor’s cloud API.
- For production use, enable GPU acceleration, restrict network access to localhost, and implement Redis caching for frequently requested completions.

## Frequently Asked Questions

### What hardware do I need to run self-hosted AI code completion?

You can run 4-bit quantized 7B models on a modern CPU with 8GB RAM, while 13B–33B models require a GPU with 8GB–24GB VRAM for real-time latency. According to the setup guide in the one-person-company context, Ollama automatically falls back to CPU inference if CUDA is unavailable, though token generation speeds drop from 50 tokens/second to 5–10 tokens/second.

### Can I use this setup with JetBrains IDEs or Vim/Neovim?

Yes. Any editor supporting OpenAI API configurations—including IntelliJ, PyCharm, Neovim with the avante.nvim plugin, or Emacs—can point to `http://localhost:8000/v1` instead of OpenAI’s official endpoint. For Vim/Neovim specifically, the Tabby LSP integration provides native inline completion support via WebSocket connections without requiring a separate browser extension.

### Is my code sent to external servers when using this configuration?

No. When you configure the API base URL to `http://localhost:8000/v1` or `ws://localhost:5000`, all inference requests remain within your local network. Unlike the Cursor entry referenced at line 561 of [`README.md`](https://github.com/cyfyifanchen/one-person-company/blob/main/README.md) in the repository, which connects to cloud infrastructure, this self-hosted architecture processes every token on your machine, satisfying strict air-gapped or compliance-sensitive development environments.

### How does CodeLlama compare to Cursor’s underlying model?

CodeLlama 13B-Instruct matches GPT-3.5-Turbo performance on HumanEval benchmarks for Python code generation, while CodeLlama 34B approaches GPT-4 on certain C++ and Java tasks. Cursor likely uses fine-tuned proprietary models or GPT-4, but the gap narrows significantly when you use larger quantized variants (33B–70B) with optimized prompts and FIM tokens for infilling.