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

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 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.

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.

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.

# 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:

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 to point to your local stack.

{
  "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.

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 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 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.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}