Why Developers Are Ditching GitHub Copilot

GitHub Copilot charges $10/month and sends every line of code you write — including your proprietary business logic, API keys typed accidentally, and unreleased features — to Microsoft's servers.

For privacy-conscious teams, that's a dealbreaker. For individual developers, the monthly subscription adds up. And for anyone who's been hit by a Copilot outage mid-sprint, the dependency on external infrastructure is a real operational risk. The good news: Open-source coding LLMs have closed the gap. In 2026, Qwen2.5-Coder 32B matches GPT-4o on multiple coding benchmarks. DeepSeek-Coder V2 reaches frontier-level code quality. These models run entirely on your hardware.

This guide covers the top 5 picks, their VRAM requirements, which GPU to pair with each, and how to set up a local Copilot replacement that works inside VS Code.

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The Top 5 Open-Source Coding LLMs

1. Qwen2.5-Coder 32B — Best Overall

Trained on 5.5 trillion tokens of code and text, it handles code generation, completion, debugging, and repair. Fill-in-the-Middle (FIM) support means it works well as an autocomplete engine, not just a chat model. Available sizes: 0.5B, 1.5B, 3B, 7B, 14B, 32B

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2. DeepSeek-Coder V2 — Best for Complex Logic

It excels at algorithmic reasoning, complex debugging, and long-context code understanding. If you're working on large codebases or intricate systems, it outperforms same-VRAM alternatives on hard problems. Available sizes: 16B, 236B (MoE — 21B active)

> Note: The 236B MoE variant requires significant offloading or a multi-GPU setup. For local use, the 16B instruct model is the practical choice — it retains most of the capability.

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3. CodeGemma 7B — Best for Low-VRAM Setups

CodeGemma is trained on 500B+ tokens of primarily code data (Python, JavaScript, Java, Kotlin, Go, C++, Rust), with specific FIM training for mid-completion autocomplete. It's the best option if you're on a laptop or budget GPU. Available sizes: 2B, 7B

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4. StarCoder2 15B — Best Open Training Data

It supports 600+ programming languages, which is genuinely unmatched. If you work across obscure stacks or legacy codebases (COBOL, Fortran, ABAP), StarCoder2 is the only model that will have seen those patterns. Available sizes: 3B, 7B, 15B

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5. GLM-4-Code 9B — Best Chinese Language + Code

Beyond its multilingual strength, GLM-4-Code performs competitively on general coding benchmarks and runs efficiently on consumer hardware. Its 9B parameter count hits a useful VRAM sweet spot. Available sizes: 9B

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Full Comparison Table

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Quick Setup: VS Code + Ollama + Continue.dev

This is the local Copilot stack. Continue.dev is a VS Code extension that integrates any local Ollama model as autocomplete and chat. Setup takes under 10 minutes.

Step 1: Install Ollama

# macOS
brew install ollama

Linux
curl -fsSL https://ollama.com/install.sh | sh

Windows: download installer from ollama.com/download

Step 2: Pull a Coding Model

Pick based on your GPU (see VRAM table above):

# Best overall — needs 18GB VRAM (RTX 3090 / RTX 4090)
ollama pull qwen2.5-coder:32b

Best for 8GB VRAM (RTX 4060 Ti, M2 Pro)
ollama pull qwen2.5-coder:7b

Best for laptops / 6GB VRAM
ollama pull codegemma:7b

Best for complex debugging (needs 10GB VRAM)
ollama pull deepseek-coder-v2:16b

Step 3: Install Continue.dev in VS Code

Step 4: Configure Continue for Your Model

Open your ~/.continue/config.json (Continue opens it for you) and configure:

{
  "models": [
    {
      "title": "Qwen2.5-Coder 32B",
      "provider": "ollama",
      "model": "qwen2.5-coder:32b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5-Coder 7B (fast autocomplete)",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  }
}

> Pro tip: Use different models for chat (large, quality) vs autocomplete (small, fast). Qwen2.5-Coder 32B for chat + 7B for tab completion is the optimal split.

What You Get

• Tab autocomplete — inline code suggestions as you type (like Copilot)
• Chat panel — explain, refactor, write tests, fix bugs with context from your file
• Highlight + ask — select any code, ask questions inline
• Codebase context — Continue can index your repo for @codebase queries

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Budget Picks: What to Buy

~$400 Budget: Run 7B Models Well

The RTX 3060 12GB is the entry-level sweet spot for coding LLMs. 12GB VRAM fits Qwen2.5-Coder 7B at Q8 (~8GB) for high-quality autocomplete, and CodeGemma 7B comfortably. What you get:

• Qwen2.5-Coder 7B Q8 — fast, high-quality autocomplete in VS Code
• CodeGemma 7B Q4 — lean autocomplete for battery-conscious laptop use
• StarCoder2 7B Q8 — if you need broad language coverage

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~$1,600 Budget: Run 33B Models Comfortably

Wait — at $1,600 total, you can actually do better than a single mid-range GPU. A used RTX 4090 24GB (~$1,100-1,300) gives you the single best consumer GPU for coding LLMs at this price. RTX 4090 24GB ($1,100-1,300 used):

• Runs Qwen2.5-Coder 32B at Q4 (~18GB) — GPT-4o-level coding quality locally
• Runs DeepSeek-Coder V2 16B at Q8 (17GB) for complex logic tasks
• ~55-65 tok/s on 7B Q4 — blazing fast autocomplete
• Handles StarCoder2 15B at Q8 (~16GB) easily

• Same 24GB VRAM as RTX 4090, runs all the same models
• ~35-45 tok/s on 7B Q4 — still fast enough for autocomplete
• 30% slower than RTX 4090 overall, but 40-60% cheaper

For more detail on GPU selection: Best GPU for Running LLMs Locally

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Which Model Should You Start With?

VRAM vs Quality Trade-offs (Quantization)

If you're new to quantization: lower = smaller file, faster load, slightly lower quality. Q4 is the standard sweet spot. Q8 is close to original quality.

For a full breakdown: Quantization Guide: Q4, Q5, Q8 Explained Rule of thumb for coding models:

• Q4 — good for autocomplete (speed matters more)
• Q6 or Q8 — better for complex multi-file refactoring (quality matters more)

Use our VRAM Calculator to check exactly which quantization level fits your GPU for each model.

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The Bottom Line

Your code stays on your hardware. No subscriptions. No outages. No data leaks.

Related Guides

• Best GPU for Running LLMs Locally — which GPU to buy at each budget
• Quantization Guide: Q4, Q5, Q8 Explained — how quantization affects VRAM and quality
• Getting Started with Ollama — install and run your first model
• VRAM Calculator — find what fits your specific GPU