Self-Hosted AI Coding Assistants in 2026: Tabby, Continue.dev + Ollama, and Void Editor — Setup Guide & Comparison

You’ve seen what GitHub Copilot and Cursor can do. Now you want that experience — fast inline completions, multi-file chat, agent-style edits — without routing your proprietary code through someone else’s cloud. That’s not a niche concern anymore. Regulatory requirements, IP agreements, air-gap environments, and straightforward cost math are all pushing teams toward self-hosted AI coding assistants.

The good news: the tooling has matured significantly. In 2026, you have three serious, production-ready options that cover very different operational profiles: Tabby (the turnkey team solution), Continue.dev paired with Ollama (the flexible developer stack), and Void Editor (the open-source IDE-first approach). This guide walks through all three — setup steps, hardware requirements, honest trade-offs — plus a comparison table to help you match the right tool to your situation.

If you’re evaluating cloud-first alternatives too, the best AI coding assistants comparison and the open-source Cursor alternatives guide cover that territory in depth.

Why Self-Host at All?

The cloud-based tools are genuinely good. So the question deserves a direct answer: when does the overhead of self-hosting pay off?

You should seriously consider self-hosting if:

• Your code is confidential by contract or regulation. Defense contractors, financial institutions, and healthcare companies routinely prohibit third-party cloud transmission of source code. Self-hosting is the only compliant path.
• You’re working in an air-gapped environment. Classified networks, secure research facilities, and offline lab environments can’t use cloud APIs by definition. A local model is the only option.
• Team scale makes per-seat pricing painful. At 20+ developers, $10–$20/seat/month adds up quickly. A one-time GPU investment can reach breakeven within a year.
• You want full audit control. When your inference server is on your infrastructure, you own the logs, you control data retention, and you can demonstrate to auditors exactly what data touched what system.

Where cloud tools still win:

• Raw model quality, especially on complex reasoning and novel API usage
• Zero maintenance overhead
• Instant access to the latest model releases

The gap in model quality between local and frontier cloud models is real but narrower than it was 18 months ago — particularly for everyday code completion and refactoring tasks. For teams with strong privacy requirements, that trade-off is increasingly acceptable.

Option 1: Tabby — The Team-First Self-Hosted Copilot

Tabby is the most purpose-built tool in this space. It’s designed to be a self-hosted GitHub Copilot replacement: ships as a single binary or Docker container, manages users through a built-in admin panel, and provides IDE extensions for VS Code, JetBrains, Vim/Neovim, and Eclipse.

What Tabby Does

• Repository context indexing: Tabby can index your codebase and surface relevant snippets from your own code as additional context during inference. This matters a lot for large monorepos where the model needs to understand your internal libraries, patterns, and naming conventions.
• OpenAPI-compatible endpoint: Any tool that understands the OpenAI API format can talk to a Tabby server.
• Multi-user and team management: API token management, user accounts, and usage analytics — all in a self-hosted admin dashboard.
• LDAP authentication: Added in recent versions, enabling integration with corporate directory services.
• No external dependencies at runtime: After the initial model download, Tabby operates entirely offline.

Tabby Quick Setup (Docker)

# Pull the image and serve with CUDA GPU support
docker run -it --gpus all \
  -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve \
  --model TabbyML/DeepseekCoder-6.7B-instruct \
  --device cuda

For Apple Silicon (Metal acceleration):

docker run -it \
  -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve \
  --model TabbyML/DeepseekCoder-6.7B-instruct \
  --device metal

After the server is running, install the Tabby VS Code extension, open Settings → Tabby, and point the endpoint URL to http://your-server-ip:8080. The extension handles authentication via an API token you generate in the Tabby admin dashboard at http://your-server-ip:8080/admin.

Tabby Pros and Cons

Pros:

• Fastest path to a team-wide deployment with proper access control
• Repository indexing substantially improves completion relevance for internal codebases
• Turnkey Docker deployment with no external database required
• Regular release cadence with enterprise-focused features

Cons:

• Less flexibility in model choice than Ollama (constrained to Tabby-supported model formats)
• Admin UI is functional but minimal compared to commercial tools
• CPU-only performance is slow for real-time completion; a GPU is effectively required for team use

Best for: Engineering teams of 5–50 developers who need centralized control, usage tracking, and LDAP integration without building a custom inference stack.

Option 2: Continue.dev + Ollama — Maximum Flexibility

If Tabby is the appliance approach, the Continue.dev + Ollama stack is the “configure everything yourself” approach — and it’s remarkably capable.

How the Stack Works

Ollama handles local model management and serving. Under the hood it wraps llama.cpp, exposes an OpenAI-compatible REST API, and makes downloading and switching models as simple as ollama pull deepseek-coder-v2. The model library includes Llama 3, Mistral, DeepSeek Coder V2, Qwen 2.5 Coder, CodeGemma, and dozens of others.

Continue.dev is a VS Code and JetBrains extension that adds chat, inline edit, and multi-file agent capabilities to your editor. It’s model-agnostic: you tell it which OpenAI-compatible endpoint to call, and it handles the rest.

Continue.dev + Ollama Quick Setup

Step 1: Install Ollama and pull a model

# Install Ollama (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh

# Pull a code-focused model
ollama pull qwen2.5-coder:7b
# or for higher quality (requires more VRAM):
ollama pull deepseek-coder-v2:16b

Step 2: Configure Continue.dev

Install the Continue.dev VS Code extension. Then edit ~/.continue/config.json:

{
  "models": [
    {
      "title": "Qwen 2.5 Coder 7B (Local)",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Autocomplete",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  }
}

Step 3: Verify

Open VS Code, open the Continue panel (Ctrl+Shift+P → “Continue: Open Chat”), and try asking it to explain or edit some code. If it responds, you’re fully operational with a private, offline AI coding assistant.

Continue.dev + Ollama Pros and Cons

Pros:

• Complete model flexibility — swap models in seconds without touching the editor
• Works entirely offline after initial model downloads
• Ollama’s model library is broad and actively maintained
• No licensing cost whatsoever

Cons:

• No built-in team management, user accounts, or usage analytics
• Each developer manages their own local setup (or you build a shared Ollama server)
• Model quality ceiling is limited by what can run locally on available hardware

Best for: Individual developers, small teams where everyone is technical, or anyone who wants to experiment with multiple models for different tasks without infrastructure overhead.

For context on which models perform best on code tasks, the best open-source LLMs guide covers the current local model landscape.

Option 3: Void Editor — Open-Source Cursor, Local Models Built In

Void Editor takes a different philosophy: instead of a plugin for VS Code, it’s a standalone open-source IDE — a fork of VS Code — built with AI-first features at the core and explicit support for local model connections. Think of it as the open-source answer to Cursor.

What Void Editor Does

• VS Code DNA: Because it’s built on the same open-source VS Code base, it’s immediately familiar. Your extensions, keybindings, and themes transfer.
• Built-in AI panel: Inline edits, contextual chat, and checkpoint management (model-applied diffs you can accept or roll back) are native UI elements, not a plugin afterthought.
• Native local model support: Connect directly to Ollama, any OpenAI-compatible endpoint, or major cloud providers (OpenAI, Anthropic, Google) — all from the same interface. No third-party routing when using local endpoints.
• Data stays local by default when configured for local models: Void routes completions to whatever endpoint you configure. Point it at a local Ollama server and your code never leaves your machine.
• Fast Apply: Void is designed for high-performance application of model-generated edits, even on large files.
• Open source (MIT): The full codebase is auditable. For teams with strict security review requirements, this matters.

Void Editor + Ollama Quick Setup

1. Download Void Editor from voideditor.com (available for macOS, Windows, Linux).
2. Make sure Ollama is running locally with your preferred model (ollama serve and ollama pull qwen2.5-coder:7b).
3. Open Void → Settings → AI Models → Add Provider → select Ollama → set base URL to http://localhost:11434.
4. Select the model from the dropdown and start coding.

Void’s inline edit experience (select code → Ctrl+K → describe what you want) feels very close to Cursor’s equivalent workflow, with the difference that the model is running on your own hardware.

Void Editor Pros and Cons

Pros:

• IDE-native AI experience with no plugin required
• Flexible provider support: local models and cloud APIs in one tool
• Open source and auditable
• Checkpoint system makes model-generated changes reversible

Cons:

• Younger project than VS Code + Continue; some ecosystem polish is still developing
• Extensions compatibility: most VS Code extensions work, but edge cases exist since it’s a fork
• Team deployment is self-managed — no built-in team admin or usage analytics

Best for: Developers who want a Cursor-like IDE experience but with open-source transparency and local model support baked in.

Side-by-Side Comparison

Hardware Requirements and GPU Recommendations

Hardware is where self-hosting gets real. The table below reflects practical experience with llama.cpp / Ollama-based deployments. VRAM requirements vary by quantization method and context window settings.

Consumer GPU Picks

For individual developers and small teams, NVIDIA GeForce GPUs are the most practical option given broad driver support and CUDA ecosystem maturity.

• RTX 4060 (8 GB VRAM): Minimum viable GPU for 7B models. Comfortable for individual use, noticeably slow for 13B. Good entry point on a budget. Search on Amazon →
• RTX 4070 / 4070 Ti (12 GB VRAM): The practical sweet spot for most developers. Runs 7B models fluidly, handles 13B models comfortably at reduced context. Search on Amazon →
• RTX 4090 (24 GB VRAM): Currently the highest VRAM available in a single consumer card. Runs 34B models in Q4 with room to spare. If budget allows, this is the ceiling for single-GPU developer setups. Search on Amazon →

Apple Silicon

For macOS users, Apple Silicon’s unified memory means the GPU and CPU share the same memory pool. An M2 Pro or M3 Pro with 16–18 GB unified memory runs 7B models with good throughput. An M2 Max / M3 Max with 32–40 GB is a compelling option for 13B–34B models. Ollama, Tabby, and Void all support Metal acceleration on Apple Silicon.

Team Server Considerations

For deploying Tabby or a shared Ollama server for a team, you’ll want a dedicated Linux server. A workstation-class machine with a single RTX 4090 (24 GB) handles small teams of 5–10 developers on 13B models. Larger teams or higher-quality 34B models benefit from multi-GPU configurations.

AMD ROCm support has improved in recent Ollama and llama.cpp releases, making RX 7900 XTX (24 GB VRAM) a viable lower-cost alternative to the RTX 4090 for budget-conscious team setups — though CUDA tooling remains better supported across the ecosystem.

Pairing Self-Hosted Completion with AI Code Review

Running local models for completion doesn’t mean you’re locked out of AI-assisted code review. Cloud-based review tools like CodeRabbit or GitHub Copilot’s built-in reviewer can complement your self-hosted completion workflow — they see PR diffs, not your live code stream. Our AI code review tools guide covers the options in detail, including self-hosted review approaches.

For teams building structured workflows around AI assistance, the AI pair programming best practices guide covers how to integrate AI tools into review loops without creating over-reliance or quality regressions.

Which Tool Should You Choose?

Choose Tabby if:

• You’re deploying for a team that needs centralized management
• You need LDAP/SSO integration for corporate directory services
• Repository context indexing against your codebase is important
• You want a turnkey solution that can be operational in an afternoon

Choose Continue.dev + Ollama if:

• You’re an individual developer or a small team of technical people
• You want to experiment with different models for different tasks
• Flexibility and model choice matter more than team management features
• You’re already using VS Code and want to stay in that ecosystem

Choose Void Editor + Ollama if:

• You want an IDE-native experience closest to Cursor’s workflow
• Open-source auditability of the editor itself matters to your security review
• You prefer a single application over a plugin + server setup
• You’re coming from Cursor and the IDE-first AI experience is important to you

All three tools can work alongside each other — there’s no reason a team couldn’t run a shared Tabby server for completions while developers also use Void for their local Ollama-powered chat sessions.

Further Reading

For developers who want a deeper understanding of how local language models work — useful context when evaluating quantization trade-offs and model selection — Build a Large Language Model (From Scratch) by Sebastian Raschka offers a practical, code-first introduction. For the infrastructure and operational side of running AI in production, Designing Machine Learning Systems by Chip Huyen covers the systems thinking that applies equally well to self-hosted inference deployments.

FAQ

Q: What is the best self-hosted AI coding assistant for a team?
Tabby — centralized admin, repository indexing, LDAP support, and a turnkey Docker deployment.

Q: How do I set up Continue.dev with Ollama?
Install Ollama, pull a code model (ollama pull qwen2.5-coder:7b), install the Continue.dev extension, and point config.json at http://localhost:11434. Done.

Q: What GPU do I need?
RTX 4070 (12 GB) for individual use with 7B–13B models; RTX 4090 (24 GB) for 34B models or small team servers.

Q: Is Void Editor open source?
Yes, MIT licensed, VS Code-based fork with native local model support built in.

Q: Can I run completely offline?
Yes. All three tools operate fully offline after initial model download — no cloud dependency during coding sessions.

Q: How does local model quality compare to Copilot?
7B models lag noticeably on complex tasks; 34B+ models are competitive on day-to-day coding work.