GitHub Copilot is $19/month now. Every line you write goes to Microsoft. And it still hallucinates imports. Local coding LLMs have caught up. Qwen 3.5 Coder 32B scores above 90% on HumanEval. DeepSeek V3.2 reasons through complex architecture like a senior engineer. These models run entirely on your hardware — no cloud, no subscription, no data leaks.

This guide covers the six best local LLMs for coding in 2026, with VRAM requirements, benchmark scores, Ollama setup commands, and a recommendation for every hardware tier.

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The 2026 Coding LLM Landscape

Two years ago, running a competitive coding model locally meant compromising on quality. That gap has closed. Open-weight models in 2026 match or exceed GPT-4 (2023 vintage) on standard coding benchmarks — and the gap to frontier models is narrowing fast.

What changed: Qwen 3.5 Coder replaced the Qwen 2.5 line with significantly better instruction following. DeepSeek V3.2's MoE architecture delivers 70B-class reasoning at 37B active parameters. Codestral got a 2025 update purpose-built for fill-in-the-middle autocomplete. Key benchmarks used in this guide:

• HumanEval — 164 Python programming problems, pass@1 rate
• MBPP — 500 Python problems testing code generation breadth
• LiveCodeBench — Competitive coding problems sampled post-training-cutoff (no contamination)
• SWE-bench Verified — Real GitHub issues on open-source repos

Top 6 Local Coding LLMs — Ranked

1. Qwen 3.5 Coder 32B — Best Overall

Qwen 3.5 Coder 32B is the current benchmark leader among locally-runnable coding models. Alibaba's 2026 update brought significantly improved instruction following, better multi-file reasoning, and stronger fill-in-the-middle performance over its Qwen 2.5 predecessor.

It handles Python, TypeScript, Rust, Go, Java, and C++ competently. At Q4 quantization, it fits in 18GB VRAM — exactly what a used RTX 3090 or RTX 4090 provides.

ollama pull qwen3.5-coder:32b
or for smaller GPUs:
ollama pull qwen3.5-coder:7b

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2. DeepSeek V3.2 — Best for Complex Reasoning

DeepSeek V3.2 is a Mixture-of-Experts model that punches well above its VRAM cost. 236B total parameters, but only 37B activate per token. You get near-70B reasoning quality at 37B inference cost.

Where it shines: hard debugging, algorithmic reasoning, long-context code understanding. Feed it a 10,000-line codebase and ask it to trace a race condition — it handles this category better than any other locally-runnable model.

ollama pull deepseek-v3:14b
prosumer tier:
ollama pull deepseek-v3:37b

> Practical note: For most local setups, the 14B or 37B distilled variants are the right choice. The full 236B model requires enterprise hardware.

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3. Codestral 2025 — Best for Autocomplete

Mistral's Codestral 2025 is the model purpose-built for one thing: inline code completion. Its fill-in-the-middle (FIM) training makes it the best autocomplete engine at any VRAM tier. Where Qwen 3.5 Coder is better at chat-style coding tasks, Codestral dominates when you want low-latency tab completion inside your editor.

22B parameters at Q4 fits in ~13GB VRAM — a good match for the RTX 4060 Ti 16GB.

ollama pull codestral:22b

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4. Llama 4 Vega 8B — Best for Low VRAM

If you're on a laptop, a budget GPU, or need something that runs fast on an 8GB card — Llama 4 Vega 8B is the pick. Meta's latest generation 8B model improved substantially on coding over Llama 3. The 5.2GB Q4 requirement fits in most consumer GPUs, including integrated memory on M4 MacBook Pros.

It's not going to compete with Qwen 3.5 Coder 32B on hard problems. But for boilerplate generation, explaining code, writing tests, and everyday autocomplete tasks, it's fast, capable, and fits anywhere.

ollama pull llama4:8b

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5. Qwen 3-30B — Best Prosumer Pick

If you want coding quality close to Qwen 3.5 Coder 32B but want a more general-purpose model (not just coding), Qwen 3-30B is the pick. It's Alibaba's latest generation 30B model with strong coding, math, and multilingual capabilities.

It fits in the same VRAM as the Coder 32B at Q4 (~18GB) but handles a wider range of tasks beyond code. Good choice if you want one model for both coding and general assistant tasks.

ollama pull qwen3:30b

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6. GLM-4.7 — Best for Chinese/English Codebases

GLM-4.7 from Zhipu AI is the strongest option for teams working in Chinese/English mixed environments. Technical comments in Chinese, bilingual docstrings, Mandarin error messages — it handles these natively in ways Western-trained models can't match.

Beyond its language advantage, it delivers solid coding benchmark scores and runs well on RTX 3090/4090 class hardware.

ollama pull glm4.7:40b

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Full Benchmark Comparison

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Quick Setup: Ollama + VS Code in 10 Minutes

The fastest path to a local Copilot replacement uses Ollama (model runner) + Continue.dev (VS Code extension).

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

Choose based on your GPU's VRAM (see table above):

# 24GB VRAM (RTX 3090, RTX 4090) — best quality
ollama pull qwen3.5-coder:32b

16GB VRAM (RTX 4060 Ti) — good balance
ollama pull codestral:22b

8GB VRAM (RTX 4060, M4 Pro) — fast and capable
ollama pull qwen3.5-coder:7b

Laptop / no dedicated GPU
ollama pull llama4:8b

Step 3: Install Continue.dev in VS Code

For a detailed setup walkthrough, see the Ollama Beginner Guide.

Pro Tip: Two-Model Stack

Use a large model for chat and a small model for autocomplete:

{
  "models": [
    {
      "title": "Qwen 3.5 Coder 32B",
      "provider": "ollama",
      "model": "qwen3.5-coder:32b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Codestral 7B (fast autocomplete)",
      "provider": "ollama",
      "model": "codestral:7b"
  }
}

Large model handles code review, explanation, and generation tasks in the chat panel. Small model handles real-time tab completion without lag.

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Which Model for Which Task?

VRAM Reality Check

Most developers are on 8–16GB VRAM cards. Here's the honest breakdown:

• 8GB VRAM — Qwen 3.5 Coder 7B (Q4, 5GB) runs well. Llama 4 Vega 8B too. You're not running 32B models.
• 12GB VRAM — Qwen 3.5 Coder 7B at Q8 (full quality). Codestral 7B for fast autocomplete.
• 16GB VRAM — Codestral 2025 22B at Q4 (13GB). Good balance of quality and speed.
• 24GB VRAM — Qwen 3.5 Coder 32B at Q4 (18GB). This is the tier where local AI becomes a real Copilot alternative.

For quantization trade-offs (Q4 vs Q5 vs Q8), see: Quantization Guide

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Frequently Asked Questions

What is the best local LLM for coding in 2026?

Qwen 3.5 Coder 32B is the top performer overall — scoring around 91% on HumanEval and leading open-weight coding benchmarks. If you have 18GB+ VRAM (RTX 3090, RTX 4090), it's the clear default. For autocomplete specifically, Codestral 2025 22B is purpose-built for fill-in-the-middle and has lower latency.

How much VRAM do I need to run a coding LLM locally?

You can get started with 8GB VRAM using Qwen 3.5 Coder 7B at Q4 quantization (~5GB). For the best local coding experience, 24GB VRAM (RTX 3090 or RTX 4090) lets you run Qwen 3.5 Coder 32B at Q4, which competes with cloud-hosted coding models. Use the VRAM Calculator for your specific GPU.

Can I use a local LLM as a GitHub Copilot replacement?

Yes. Qwen 3.5 Coder 32B + Continue.dev in VS Code is the standard local Copilot replacement stack in 2026. You get tab autocomplete, chat-based code generation, and codebase context — entirely on your hardware. Setup takes under 10 minutes with Ollama. See the Ollama guide for step-by-step instructions.

What is the difference between a general LLM and a coding LLM?

Coding LLMs are fine-tuned on code-heavy datasets with additional training on fill-in-the-middle (FIM) tasks. This makes them better at autocomplete (predicting the middle of a function), generating syntactically correct code, and following coding instructions. General models like Llama 4 still write solid code — but dedicated models like Qwen 3.5 Coder or Codestral score 10–20 points higher on coding benchmarks.

Do local coding LLMs work offline?

Yes, once you've downloaded the model. Ollama runs entirely locally — no internet required for inference. You only need internet for the initial ollama pull to download the model file (typically 5–20GB depending on model size and quantization).

What is the best coding LLM for a laptop?

Llama 4 Vega 8B (Q4, 5.2GB VRAM) is the most capable option that runs on laptops and low-VRAM setups. On M4 MacBook Pro or M4 Pro chips with 16GB+ unified memory, you can push up to Qwen 3.5 Coder 14B (~9GB) for noticeably better results. Apple Silicon's unified memory architecture gives local LLMs more headroom than discrete GPU setups at the same memory size.

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Related Guides

• Best Open Source LLMs for Coding — in-depth VS Code + Continue.dev setup
• How to Run LLMs Locally — Beginner Guide — start here if you're new
• Ollama Complete Guide — install, configure, and run local models
• How Much VRAM for LLMs in 2026 — VRAM requirements at every tier
• VRAM Calculator — find the right model for your exact GPU