Self-Hosted AI Coding Assistant in 2026: Tabby, Ollama, and the Best Self-Hosted Copilot Options

Cloud-based AI coding tools have transformed how developers write code. But not everyone can — or should — send their code to a third-party server. Regulated industries, security-conscious engineering teams, and developers who simply value their privacy are driving a real and growing interest in self-hosted alternatives.

This guide covers the leading self-hosted AI coding assistants available in 2026: Tabby, Ollama paired with Continue.dev, LocalAI, Fauxpilot, and LM Studio. I’ll give you an honest picture of hardware requirements, integration quality, and where each tool fits best — with no invented benchmarks.

If you’re evaluating cloud-based options alongside these, see our best AI coding assistants comparison for a full picture. And if you’re specifically looking for open-source IDE alternatives to Cursor, the open source Cursor alternatives guide covers that angle in depth.

Why Self-Host Your AI Coding Assistant?

Before diving into tools, it’s worth being clear about why you’d accept the operational overhead of self-hosting:

• Data privacy and code confidentiality — Your source code never leaves your infrastructure. This matters enormously for fintech, healthcare, defense contractors, and anyone bound by strict IP agreements.
• Offline / air-gapped environments — Facilities with no external internet access can still benefit from AI-assisted development when the model runs locally.
• Cost predictability — At sufficient team scale, running your own inference hardware can undercut per-seat SaaS pricing, especially for completion-heavy workflows.
• Compliance and auditability — You control the model, the logs, and the data retention policy. Audit trails stay inside your perimeter.

The trade-off is real: self-hosted models — even large ones — generally lag behind frontier cloud models on raw code quality. The gap is narrowing fast, but it exists. What you gain in control, you give up (at least partially) in capability.

1. Tabby — The Purpose-Built Self-Hosted Copilot

Tabby is the most complete purpose-built solution in the self-hosted space. Unlike generic inference servers, it was designed from the ground up as a self-hosted GitHub Copilot replacement — complete with an admin dashboard, team management, IDE plugins, and a built-in code context index.

What it does well:

• Ships as a single self-contained binary or Docker container — no external database or cloud dependency required.
• Exposes an OpenAPI-compatible interface, making it easy to integrate with CI pipelines or custom tooling.
• IDE plugins available for VS Code, JetBrains, Vim/Neovim, and Eclipse.
• Repository context indexing: Tabby can index your codebase and surface relevant snippets to the model at inference time, improving completion relevance significantly for large monorepos.
• Enterprise-grade features: LDAP authentication (added in v0.24), GitLab MR indexing (v0.30), and a growing admin panel for managing users and usage analytics.

Hardware requirements: Tabby supports CPU-only inference, but the experience is noticeably sluggish for real-time completion. For a productive workflow:

• Minimum: NVIDIA GPU with 8 GB VRAM (RTX 3060 class) running a ~1–3B parameter model.
• Recommended: 16–24 GB VRAM (RTX 3090 / RTX 4090) for 7B–13B models that deliver meaningfully better completions.
• Apple Silicon: Tabby supports Metal acceleration; M1 Pro / M2 Pro with 16 GB unified memory gives a reasonable experience with smaller models.

Best for: Teams that want a turnkey, Copilot-like deployment they can manage centrally, with proper multi-user support and usage tracking.

2. Ollama + Continue.dev — The Flexible Stack

If Tabby is the “appliance” approach, the Ollama + Continue.dev pairing is the “build your own” approach — and it’s remarkably capable.

Ollama handles local model management and serving. It wraps llama.cpp under the hood, supports an OpenAI-compatible API, and makes pulling and running models about as easy as docker pull. As of early 2026, the model library includes Llama 3, Mistral, DeepSeek Coder, Qwen 2.5 Coder, and dozens of others — all runnable locally.

Continue.dev is a VS Code and JetBrains extension that adds chat, inline editing, and agent capabilities to your editor. It’s designed to be model-agnostic: point it at any OpenAI-compatible endpoint, including Ollama, and it works.

What the combination offers:

• Complete flexibility to swap models without touching your editor configuration.
• Chat, autocomplete, and multi-file editing (via Continue’s Agent mode) from a single extension.
• Works entirely offline once models are downloaded.
• No licensing cost beyond your hardware.

Model recommendations for code tasks:

• DeepSeek Coder V2 and Qwen 2.5 Coder are consistently rated among the best locally-runnable code models as of 2026, based on community testing and leaderboard data (EvalPlus).
• For constrained hardware (8 GB VRAM), 7B quantized models (Q4_K_M) are the practical ceiling.

Hardware requirements:

• Ollama runs on CPU (slow), NVIDIA CUDA, AMD ROCm, and Apple Silicon (Metal).
• 7B model with Q4 quantization requires approximately 4–5 GB RAM; 13B models need ~8–9 GB.
• For comfortable latency on completions, 8 GB VRAM minimum is a reasonable working floor.

Best for: Individual developers and small teams who want maximum flexibility, or want to experiment with different models for different tasks.

For a broader view of models you can run locally with this stack, see the best open source LLMs guide.

3. LocalAI — OpenAI-Compatible Inference Server

LocalAI is a drop-in OpenAI API replacement server. Where Ollama is opinionated and easy, LocalAI is more flexible and lower-level — it can run GGUF, GPTQ, ONNX, and other model formats, and supports multimodal models alongside text generation.

Strengths:

• True OpenAI API compatibility means any tool that supports OpenAI (including Continue.dev, Aider, and others) can switch to LocalAI with a single endpoint change.
• Supports a wider range of model backends than Ollama (llama.cpp, whisper.cpp, stable-diffusion.cpp, etc.).
• Docker-based deployment with GPU passthrough.
• Good choice when you need a single inference server for multiple applications (not just code completion).

Limitations:

• More configuration required than Ollama — model setup isn’t as streamlined.
• Documentation can lag behind the fast-moving codebase.

Best for: Teams already building LLM-powered internal tooling who want one server to power everything, including coding assistants.

4. Fauxpilot — Air-Gap Focused, NVIDIA-Required

Fauxpilot was one of the earliest self-hosted Copilot clones, built specifically around NVIDIA Triton Inference Server and FasterTransformer. It’s designed for organizations with strict air-gap requirements and existing NVIDIA datacenter hardware.

What sets it apart:

• Implements the GitHub Copilot API protocol directly, meaning GitHub Copilot’s official VS Code extension can point at a Fauxpilot server without modification.
• Optimized for throughput in multi-user deployments.

Honest limitations:

• NVIDIA GPU required — no CPU fallback, no AMD, no Apple Silicon.
• Setup is significantly more involved than Tabby or Ollama.
• The project’s pace of development has slowed compared to alternatives; active maintenance should be verified before committing.
• Code models available for Fauxpilot’s architecture are older than what’s now available through Ollama or Tabby.

Best for: Organizations with NVIDIA datacenter hardware, strict air-gap requirements, and the engineering bandwidth to maintain the deployment.

5. LM Studio — Local Inference with a GUI

LM Studio takes a different angle: it’s a desktop application (Mac, Windows, Linux) for downloading, managing, and running local LLMs with a graphical interface. It also exposes a local OpenAI-compatible server, which Continue.dev, Aider, or any other tool can connect to.

What it’s good at:

• Zero-CLI setup: download a model from the built-in HuggingFace browser, click run, done.
• Great for individual developers evaluating local models without terminal friction.
• The local server mode makes it a functional Ollama alternative for GUI-preferring users.

Limitations:

• Closed-source application (though free to use).
• Not designed for server or headless deployment — it’s a desktop tool.
• No multi-user or team management features.

Best for: Individual developers on Mac or Windows who want the easiest possible local LLM experience for personal use.

A Note on HuggingFace Inference Endpoints

For teams that want model control without the operational burden of running GPU hardware, HuggingFace Inference Endpoints offer a middle path: you deploy a specific model (including fine-tuned or private models) to HuggingFace-managed infrastructure, and the endpoint is accessible only to you. Code still leaves your machine, but it goes to your dedicated endpoint rather than a shared SaaS model, and you retain control over which model version runs. Pricing is consumption-based (per compute hour), so evaluate costs relative to seat-based Copilot pricing for your team size.

Honest Hardware Reality Check

The most common mistake developers make when entering the self-hosted space is underestimating hardware requirements. Here’s a practical reference:

These figures reflect community experience based on llama.cpp / Ollama deployments. Actual VRAM use varies by quantization method, context length, and model architecture. If you’re evaluating specific models, the LLM Explorer provides community-sourced hardware requirements.

Pairing Self-Hosted Assistants with Code Review

Running AI-generated code through an automated review layer is good practice regardless of whether you’re using cloud or self-hosted tools. Our AI code review tools guide covers the best options for catching security issues and style problems before they reach production — a worthwhile complement to any local coding assistant setup.

Further Reading

For developers building deeper AI literacy alongside their tooling choices, Build a Large Language Model (From Scratch) by Sebastian Raschka gives a practical, code-first understanding of how these models work — useful context when evaluating quantization trade-offs, fine-tuning options, and model selection. For a broader systems perspective on deploying AI in production, Designing Machine Learning Systems by Chip Huyen covers the infrastructure and operational concerns that matter when you’re running inference on your own hardware.

FAQ

Q: What is the best self-hosted AI coding assistant in 2026?
Tabby is the most complete turnkey option for teams; Ollama + Continue.dev is the most flexible choice for individuals.

Q: Can I run a self-hosted AI coding assistant without a GPU?
Yes, but CPU-only inference is slow for real-time completion. It’s more acceptable for chat-style interactions.

Q: Is Tabby truly air-gap compatible?
Yes — after initial model download, Tabby operates entirely locally with no external network calls required.

Q: How does self-hosted quality compare to GitHub Copilot?
Small models lag behind; 34B+ models match Copilot on many everyday tasks. The gap is real but narrowing.

Q: What’s the easiest self-hosted team setup?
Deploy Tabby via Docker on a GPU machine, install the IDE plugin on each developer’s machine, done. An afternoon’s work for most teams.