Best Self-Hosted AI for Coding: Run LLMs Locally (2026)

Cloud-based AI coding assistants like GitHub Copilot and Cursor work well — until your company’s security policy blocks them. Or until you hit rate limits during a critical sprint. Or until your proprietary codebase gets sent to a third-party server you don’t control. You might also want to explore our picks for best AI code assistants.

Self-hosted AI for coding solves all of these problems. You run the model on your own hardware, keep every line of code on your network, and pay nothing per token after the initial setup. In 2026, the local LLM ecosystem has matured to the point where self-hosted setups genuinely compete with cloud alternatives. We also cover this in our roundup of Copilot vs Cursor vs Windsurf.

We tested the best local AI models for coding alongside the tools that run them, and here is what actually works.

TL;DR: Our Top Picks

Need	Best Pick	Why
Best local coding model	Qwen3-Coder (35B)	Strongest open-weight coding model, 256K context, Apache 2.0 license
Best self-hosted team platform	Tabby	Purpose-built for teams, code completion + chat, repository indexing, SSO
Best IDE integration	Continue	Model-agnostic, works with VS Code and JetBrains, fully open-source
Best CLI tool	Aider	Deep Git integration, multi-file edits, supports 100+ languages
Best model runner	Ollama	Simplest setup, huge model library, REST API, GPU acceleration
Best GUI model runner	LM Studio	Polished desktop app, drag-and-drop model management, built-in chat

Why Self-Host Your AI Coding Assistant?

Before diving into specific tools, here is why teams are moving away from cloud-based AI coding assistants:

• Data privacy: Your code never leaves your network. No third-party server sees your proprietary logic, API keys, or internal architecture.
• No rate limits: Run as many completions as your hardware can handle. No throttling during peak hours.
• Zero per-token cost: After hardware investment, inference is free. Teams with 15-20+ developers typically see cost savings within months compared to per-seat cloud subscriptions.
• Offline access: Works without an internet connection. Code on a plane, in a secure facility, or in any air-gapped environment.
• Customization: Fine-tune models on your own codebase for completions that understand your internal libraries, coding conventions, and architecture.
• Compliance: Meet GDPR, HIPAA, SOC 2, and other regulatory requirements that prohibit sending source code to external services.

The tradeoff is hardware investment and setup time, but as we will cover below, the barrier is lower than most developers expect.

Best Local AI Models for Coding

These are the models you actually run. Each can be loaded into a model runner like Ollama or LM Studio and connected to an IDE integration like Continue or Tabby.

1. Qwen3-Coder — Best Overall Local Coding Model

Overview: Qwen3-Coder is Alibaba’s code-focused LLM, trained on 7.5 trillion tokens with 70% code data. It uses a mixture-of-experts (MoE) architecture and ships in two sizes: 35B and 480B parameters. The 35B variant runs on a single high-end GPU, while the 480B version requires a multi-GPU server.

Why it stands out: A 256K context window (extendable to 1M) lets you feed entire repositories into a single session. It supports 350+ programming languages and handles agentic workflows — meaning it can plan, execute, and iterate on coding tasks autonomously.

Hardware requirements: The 35B FP8 variant needs roughly 20GB VRAM (fits on an RTX 4090 or A6000). The 480B model requires multi-GPU setups with 200GB+ total VRAM.

Spec	Detail
Parameters	35B / 480B
Context window	256K (extendable to 1M)
Languages supported	350+
License	Apache 2.0
Best for	Agentic coding, large codebase comprehension

Pros:
– Top-tier benchmark scores rivaling GPT-4o and Claude Sonnet on coding tasks
– Apache 2.0 license allows unrestricted commercial use
– 256K context handles entire repositories in one session
– Supports agentic workflows out of the box

Cons:
– 35B model still needs a high-end GPU (20GB+ VRAM)
– 480B model is impractical for most local setups
– Newer model with a smaller community than DeepSeek or Llama

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2. DeepSeek Coder V2 — Best for Multi-Language Projects

Overview: DeepSeek Coder V2 builds on the DeepSeek-V2 base with 6 trillion additional tokens of training data. It ships in two variants: a 16B “Lite” model and a 236B full model. The 16B version runs comfortably on consumer GPUs and supports 338 programming languages with a 128K context window.

Why it stands out: The 16B Lite model hits a sweet spot between quality and hardware requirements. It scores alongside premium closed models on the Aider LLM leaderboard for code reasoning, and the MIT license makes it easy to deploy commercially.

Hardware requirements: The 16B Lite runs in roughly 10GB VRAM (4-bit quantized). The 236B model needs multi-GPU setups.

Spec	Detail
Parameters	16B (Lite) / 236B
Context window	128K
Languages supported	338
License	MIT (Lite) / DeepSeek License
Best for	Fast code completion, cross-file refactoring

Pros:
– 16B Lite runs on consumer GPUs with excellent quality
– 128K context handles large files and multi-file projects
– Strong debugging and error-fixing capabilities
– MIT license for the Lite variant

Cons:
– 236B model is too large for most local setups
– The DeepSeek License (full model) has some restrictions
– Fewer agentic capabilities compared to Qwen3-Coder

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3. Codestral — Best for Fast Code Completion

Overview: Codestral is Mistral AI’s dedicated code generation model, available in 22B and a lightweight Mamba 7B variant. It is designed specifically for low-latency code completion across 80+ programming languages, making it ideal for real-time suggestions during live editing.

Why it stands out: Codestral prioritizes speed. The 7B Mamba variant uses a state-space architecture that runs significantly faster than transformer models of similar size, making it one of the snappiest local models for tab-completion workflows.

Spec	Detail
Parameters	7B (Mamba) / 22B
Context window	32K
Languages supported	80+
License	Non-Production (free for research/testing)
Best for	Real-time code completion, low-latency suggestions

Pros:
– Very fast inference, especially the 7B Mamba variant
– Low VRAM requirements (7B fits in 4-5GB quantized)
– Good quality for inline completions and short generations
– Works well as a dedicated autocomplete model alongside a larger chat model

Cons:
– Non-production license limits commercial use
– 32K context is small compared to competitors
– Not as strong for complex, multi-step coding tasks
– Fewer languages than DeepSeek or Qwen

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4. Llama 4 — Best General-Purpose Option

Overview: Meta’s Llama 4 is not a coding-specific model, but its open-weight architecture and strong general reasoning make it a solid choice for developers who need one model for both coding and non-coding tasks. It ships in multiple sizes and is compatible with every major model runner.

Spec	Detail
Parameters	Multiple sizes available
Context window	128K+
Languages supported	Broad (not coding-specific)
License	Meta Open License
Best for	General-purpose coding + non-coding tasks

Pros:
– Strong general reasoning benefits complex code logic
– Broad ecosystem support (Ollama, LM Studio, vLLM, etc.)
– Active community and frequent updates from Meta
– Good balance of coding and natural language abilities

Cons:
– Not specifically tuned for code, so dedicated coding models outperform it on benchmarks
– Larger variants require significant hardware
– Meta’s license has some use restrictions at scale

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5. GLM-4-32B — Best for Code Analysis

Overview: Tsinghua University’s Zhipu AI released GLM-4-32B, a 32-billion-parameter model pretrained on 15 trillion tokens of reasoning-heavy data. It excels at code analysis, multi-step reasoning, and function-call outputs, performing comparably to GPT-4o and DeepSeek-V3 on coding benchmarks.

Spec	Detail
Parameters	32B
Context window	128K
License	Open
Best for	Code analysis, reasoning, debugging

Pros:
– Excellent multi-step reasoning for tracing logic and suggesting improvements
– Strong function-calling capabilities for tool use
– 32B size fits on a single high-end GPU
– Open license for commercial use

Cons:
– Smaller community outside of China
– Less documentation in English compared to Llama or DeepSeek
– Not specifically a code-first model

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Best Self-Hosted Coding Assistant Platforms

These are the tools that sit between your model and your IDE. They handle code completion, chat, context gathering, and team management.

1. Tabby — Best for Teams

Overview: Tabby is an open-source, self-hosted AI coding assistant built specifically for teams that cannot send code to external servers. With 32K+ GitHub stars, it is one of the most mature self-hosted coding platforms available. It runs entirely on your infrastructure and offers code completion, an answer engine, inline chat, and repository indexing.

Key features:
– Code completion engine with real-time, context-aware suggestions
– Answer Engine for asking questions about your codebase directly in the IDE
– Inline chat for AI-driven discussions without leaving your editor
– Context Providers that pull data from documentation, configs, and external sources
– Repository indexing including GitLab Merge Request context
– Team management with SSO, LDAP authentication, and usage analytics
– IDE support for VS Code, JetBrains, Vim/Neovim. We also cover this topic in our guide to AI for VS Code.

Pricing: Free and open-source to self-host. Enterprise pricing available on request for team management and support.

Pros:
– Purpose-built for team use with admin controls and analytics
– No external dependencies — self-contained with no cloud DBMS required
– Works with consumer-grade GPUs
– OpenAPI interface for custom integrations

Cons:
– Requires infrastructure setup and maintenance
– Model quality depends on which LLM you connect
– Enterprise features require contacting sales for pricing
– Smaller plugin ecosystem than Continue

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2. Continue — Best IDE Integration

Overview: Continue is an open-source AI code assistant that brings code completion, chat, and editing capabilities directly into VS Code and JetBrains. Its model-agnostic architecture lets you connect any LLM — local models via Ollama or LM Studio, or cloud providers like OpenAI and Anthropic. Enterprise users include Siemens and Morningstar.

Key features:
– Tab autocomplete with any connected model
– @codebase context provider for automatic retrieval of relevant code snippets
– @docs context provider for indexing and querying documentation
– Model-agnostic: connect Ollama, LM Studio, llamafile, or any OpenAI-compatible endpoint
– Local-first configuration via config.yaml (can be checked into Git)
– Full air-gapped operation when paired with local models

Pricing:
| Plan | Price | Notes |
|——|——-|——-|
| Solo | Free | Full open-source extension |
| Team | $10/dev/month | Centralized config, shared agents |
| Enterprise | Custom | Governance, priority support, self-hosting |

Pros:
– Completely free and open-source core
– Works with any model provider (local or cloud)
– Clean IDE integration for both VS Code and JetBrains
– Air-gapped operation with zero data leakage when using local models

Cons:
– No built-in model serving — you need a separate runner (Ollama, etc.)
– Team features require a paid Hub subscription
– Setup requires configuring both the extension and a model backend
– Less mature than Tabby for enterprise team management

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3. Cline — Best for Agentic Workflows

Overview: Cline is an open-source agentic coding assistant that works inside VS Code. It follows a plan-review-run workflow: describe a task, review proposed changes, and approve execution step by step. It can edit files, run terminal commands, browse your local dev server, and connect to external services via MCP tools. Cline raised $32M in funding in 2025 and is expanding to JetBrains and Neovim. We also cover this topic in our guide to AI for Python coding.

Key features:
– Agentic plan-review-run workflow for autonomous task completion
– File editing, terminal command execution, and local server browsing
– MCP tool integration for connecting to external services
– Model-agnostic with support for local models via Ollama
– Step-by-step approval for reviewing every change before execution

Pricing: Free and open-source. You pay your model provider directly.

Pros:
– Powerful agentic capabilities for complex, multi-step tasks
– Step-by-step approval keeps you in control
– Well-funded with active development and IDE expansion
– Near-zero marginal cost when paired with local models

Cons:
– VS Code only (JetBrains and Neovim support still in progress)
– Agentic workflows consume more tokens than simple completion
– Requires a capable model (smaller models struggle with agentic tasks)
– Less focused on code completion, more on task automation

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4. Aider — Best CLI Tool

Overview: Aider is the oldest and most popular open-source CLI tool for AI-assisted coding, with 39K+ GitHub stars and 4.1M+ installations. It lives in your terminal and can directly edit files in your repository, create new files, run linters and tests, and commit changes — all through natural language conversation.

Key features:
– Deep Git integration with automatic descriptive commits
– Repository mapping for whole-codebase context
– Multi-file editing via natural language instructions
– Supports 100+ programming languages
– Multiple chat modes: code, architect, ask, help
– Voice-to-code support for hands-free operation
– Automatic linting and test execution with self-fixing
– Works with any LLM (Claude, GPT, DeepSeek, local models via Ollama)

Pricing: Free and open-source. You pay your model provider directly.

Pros:
– Mature and battle-tested with the largest user base in its category
– Automatic Git commits keep your history clean
– Repository mapping gives the LLM full project context
– Model-agnostic with excellent local model support

Cons:
– Terminal-only interface (no GUI, though browser mode exists)
– Learning curve for developers used to IDE-based tools
– Some models work much better than others (results vary by LLM)
– Heavy context usage can be slow with smaller local models

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Best Model Runners

You need a runtime to actually load and serve your chosen model. These tools handle the heavy lifting of inference.

1. Ollama — Best Overall Runner

Overview: Ollama is the most popular tool for downloading, managing, and running LLMs locally. Built on llama.cpp, it delivers fast token generation with intelligent memory management and GPU acceleration for NVIDIA (CUDA), Apple Silicon (Metal), and AMD (ROCm). One command — ollama run qwen3-coder — downloads and starts a model.

Key features:
– One-command model download and execution
– REST API on port 11434 (OpenAI-compatible)
– GPU acceleration for NVIDIA, AMD, and Apple Silicon
– Modelfile system for custom model configurations
– Tool calling and function execution support
– Quantization support from 1.5-bit to 8-bit (GGUF format)
– Integrations with Continue, Tabby, Aider, and more

Pricing: Free and open-source.

Performance benchmarks (approximate, RTX 4090):
| Model Size | Eval Speed |
|———–|————|
| 8B (4-bit) | ~95 tokens/sec |
| 32B (4-bit) | ~34 tokens/sec |
| 70B (4-bit) | ~15 tokens/sec |

Pros:
– Simplest setup of any model runner
– Massive model library at ollama.com/library
– Broad hardware support including NPU acceleration
– Active development with frequent model additions

Cons:
– CLI-focused (no built-in GUI)
– Less fine-grained control than raw llama.cpp
– Single-user by default (needs configuration for multi-user serving)
– Limited batch inference capabilities

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2. LM Studio — Best GUI Runner

Overview: LM Studio is a desktop application for discovering, downloading, and running local LLMs with a polished graphical interface. It includes a built-in chat UI, a local API server (OpenAI-compatible), RAG document chat, and MCP server support. Available on macOS, Windows, and Linux.

Key features:
– Drag-and-drop model management from Hugging Face
– Built-in chat interface for testing models
– Local API server with OpenAI-compatible endpoints
– Anthropic-compatible endpoint for Claude Code integration
– RAG document chat for querying local files
– Python and TypeScript SDKs for developer integration
– GPU offload controls for performance tuning

Pricing: Free for personal use. Enterprise tier available for organizational deployment.

Pros:
– Most user-friendly interface for running local models
– No terminal knowledge required
– Built-in chat makes model testing immediate
– Good model discovery via Hugging Face integration

Cons:
– Closed-source (unlike Ollama)
– Less suitable for automation and scripting
– Linux support has historically lagged behind macOS and Windows
– Enterprise licensing terms may apply for commercial use

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Recommended Self-Hosted Stacks

Here are three complete setups depending on your situation:

Solo Developer Stack

Component	Tool
Model	Qwen3-Coder 35B or DeepSeek Coder V2 16B
Runner	Ollama
IDE integration	Continue (VS Code/JetBrains)
CLI tool	Aider (optional)
Hardware	RTX 4090 (24GB VRAM) or Mac with 32GB+ unified memory

Small Team Stack (5-20 developers)

Component	Tool
Model	Qwen3-Coder 35B
Runner	Ollama (on a shared GPU server)
Platform	Tabby (for team management and analytics)
IDE integration	Tabby’s VS Code/JetBrains extension
Hardware	Server with 1-2x A100 (80GB) or equivalent

Enterprise Stack (20+ developers)

Component	Tool
Model	Qwen3-Coder 35B + Codestral 7B (for fast completions)
Runner	vLLM or Ollama (for production serving)
Platform	Tabby Enterprise (SSO, LDAP, analytics)
IDE integration	Tabby + Continue (for developer flexibility)
Hardware	Multi-GPU server cluster (A100s or H100s)

Hardware Requirements Guide

The most common question about self-hosted AI is “what hardware do I need?” Here is a practical breakdown:

Setup	Minimum Hardware	Models You Can Run
Budget	16GB RAM, integrated GPU	1-3B models (basic completions only)
Consumer	RTX 3060 12GB or Mac M1 16GB	7-8B models (decent completions)
Enthusiast	RTX 4090 24GB or Mac M2 Pro 32GB	16-35B models (strong completions and chat)
Professional	A6000 48GB or Mac M4 Max 128GB	35-70B models (competitive with cloud)
Server	2x A100 80GB or equivalent	70B+ models (near cloud quality)

Key rules of thumb:
– 4-bit quantized models need roughly 0.5GB VRAM per billion parameters
– Apple Silicon unified memory can substitute for VRAM but runs 2-3x slower than dedicated GPUs
– For code completion (short outputs), smaller models feel fast even on modest hardware
– For chat and complex reasoning, bigger models with more VRAM make a noticeable difference

Quick Comparison: Self-Hosted vs. Cloud AI Coding Assistants

Factor	Self-Hosted	Cloud (Copilot, Cursor)
Data privacy	Full control, code stays local	Code sent to third-party servers
Cost (1 dev)	Higher upfront, free ongoing	$10-40/month per seat
Cost (20 devs)	Lower total cost after setup	$200-800/month ongoing
Setup time	1-4 hours	Minutes
Quality (best models)	85-95% of cloud quality	Baseline
Offline access	Yes	No
Customization	Fine-tune on your codebase	Limited
Maintenance	You handle updates and hardware	Managed by provider

Final Verdict

The best self-hosted AI for coding depends on your situation:

• Best local model for most developers: Qwen3-Coder 35B delivers near-cloud quality with an Apache 2.0 license and fits on a single high-end GPU.
• Need a lighter model? DeepSeek Coder V2 16B runs on consumer GPUs and still produces strong results.
• Building for a team? Tabby provides the management, analytics, and SSO that team deployments require.
• Want maximum IDE flexibility? Continue connects any model to VS Code or JetBrains with zero vendor lock-in.
• Prefer the terminal? Aider is the most mature CLI coding tool with excellent Git integration.
• Just getting started? Install Ollama, run ollama run deepseek-coder-v2:16b, connect Continue in VS Code, and you will have a fully private coding assistant running in under 30 minutes.

The self-hosted AI coding ecosystem in 2026 is no longer a compromise. For teams that care about privacy, cost control, and customization, it is the better choice.