I Tried Replacing GitHub Copilot with Local AI — Here’s What Happened (Docker + GPU) – ██FR█████ █INTELL███████████

This content originally appeared on DEV Community and was authored by Avisek Dey

A practical guide to running a private, GPU-accelerated coding assistant locally using Docker Desktop — no API costs, no data leaving your machine.

Setup time: ~30–60 minutes

My Story

I’m a professional Django developer based in India.

Like most developers, I was using cloud-based AI tools for coding assistance — paying for API credits, sending my code to third-party servers, and depending on a stable internet connection just to get a code suggestion.

Then one day I was exploring Docker Desktop and noticed two new things in the sidebar — Models and MCP Toolkit.

I had no idea what they were.

A few hours of tinkering later, I had a fully local AI coding assistant running on my laptop:

Free
Private (no code leaves my machine)
Works offline
GPU-accelerated (~273ms response time)

No GitHub Copilot subscription. No API costs.

This is exactly how I built it — including all the mistakes I made along the way.

Before We Start (Important Reality Check)

Let’s be honest:

This is NOT a perfect replacement for GitHub Copilot.

Cloud models (like GPT-4/5-level) are still:

better at reasoning
better at large codebases
more consistent

But…

For most day-to-day coding tasks, a local setup like this is:

fast enough
smart enough
and WAY more private

Think of this as a practical alternative, not a 1:1 replacement.

Why Run AI Locally?

Problem with Cloud AI	Local AI Solution
Costly at scale ($10–30/million tokens)	Completely free
Needs internet	Works offline
Code sent to third-party servers	Stays on your machine
Network latency	GPU-accelerated (~273ms)

My Setup

This guide is based on my machine — adjust based on your hardware.

Component	Spec
Laptop	Lenovo IdeaPad Pro 5
CPU	Intel Core Ultra 9 185H
GPU	6GB NVIDIA
RAM	32GB
OS	Windows 11

Minimum Setup Recommendation

Hardware	What to Expect
No GPU	Works, but slow (~2–5s responses)
8GB RAM	Very limited models only
16GB RAM	Usable
32GB + GPU	Ideal

Step 1 — Install Docker Desktop

Install Docker Desktop with AI features enabled:

https://www.docker.com/products/docker-desktop

Make sure you see Models and MCP Toolkit in the left sidebar.

Step 2 — Understanding Model Selection

Before pulling any model, you need to understand three things. This took me a while to figure out — so I’ll keep it quick.

Parameters = Brain Size

7B  → Good for most coding tasks ✅
30B → Needs 16GB+ VRAM
70B → High-end machines only

Quantization = Compression

Think of it like image compression — smaller file, slight quality trade-off.

F16  → Full precision, largest file
Q4_0 → 4x compressed, best balance ✅
Q2   → Smallest, noticeable quality loss

The Golden Rule — RAM vs VRAM

Fits in VRAM (6GB)?  → GPU  ⚡ ~273ms
Spills to RAM?       → CPU  🐢 ~3000ms
Too big for RAM?     → ❌ Won't run

Step 3 — Pull the Right Model

My Pick: `qwen2.5:7B-Q4_0`

Property	Value	Why
Parameters	7.62B	Smart enough for coding
Quantization	Q4_0	4x compressed, great quality
Size	4.12 GiB	Fits perfectly in 6GB VRAM

Steps:

Docker Desktop → Models
Search qwen2.5
Find qwen2.5:7B-Q4_0 → click Pull

~4GB download. Grab a coffee.

Step 4 — Enable GPU-Accelerated Inference (CRITICAL)

Most guides miss this step entirely.

By default Docker Model Runner uses CPU only. One checkbox changes everything.

Go to: Docker Desktop → Settings → AI

Enable all three:

Enable Docker Model Runner
Enable host-side TCP support → Port: 12434
Enable GPU-backed inference

Click Apply.

GPU inference downloads additional components — takes a few minutes the first time.

Verify It’s Working

Open your browser:

http://localhost:12434/engines/v1/models

You should see:

{
  "object": "list",
  "data": [
    {
      "id": "docker.io/ai/qwen2.5:7B-Q4_0",
      "object": "model",
      "owned_by": "docker"
    }
  ]
}

Check Response Speed

Go to Docker Desktop → Models → Requests tab after sending a prompt:

Mode	Speed
GPU	~273ms
CPU	~3000ms

That’s a 10x speedup from one checkbox.

Step 5 — Connect VS Code via Continue.dev

Docker Models exposes a local OpenAI-compatible API at:

http://localhost:12434/engines/v1

Key insight: any tool that supports OpenAI’s API works here — just change the URL from OpenAI’s server to localhost.

Install Continue.dev

VS Code → Extensions (Ctrl+Shift+X)
Search Continue
Install Continue – open-source AI code agent

Configure It

Open C:\Users\<yourname>\.continue\config.yaml and paste:

name: Local Config
version: 1.0.0
schema: v1
models:
  - name: Qwen2.5 Coder Local
    provider: openai
    model: ai/qwen2.5:7B-Q4_0
    apiBase: http://localhost:12434/engines/v1
    apiKey: docker

Why provider: openai? Docker’s API speaks the OpenAI protocol — same language, different address.

Why apiKey: docker? Just a placeholder — localhost needs no real auth.

Windows PowerShell Fix (if needed)

If you hit a script execution error:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Test It!

If it responds — you’re done.

Real Comparison — Copilot vs Local AI

Prompt I tested:

Write a Django REST Framework viewset for a User model
with JWT authentication and permission classes

GitHub Copilot output:

Clean, complete, production-ready code with proper imports, docstrings, and edge case handling. Roughly 60 lines, zero follow-up needed.

Local Qwen 7B output:

from rest_framework import viewsets, permissions
from rest_framework_simplejwt.authentication import JWTAuthentication
from .models import User
from .serializers import UserSerializer

class UserViewSet(viewsets.ModelViewSet):
    queryset = User.objects.all()
    serializer_class = UserSerializer
    authentication_classes = [JWTAuthentication]
    permission_classes = [permissions.IsAuthenticated]

    def get_queryset(self):
        # Users can only see their own data
        return User.objects.filter(id=self.request.user.id)

Solid, functional, correct — but less complete than Copilot. Needed a follow-up prompt for edge cases.

Verdict:

Feature	Copilot	Local Qwen 7B
Speed	Fast	Fast (GPU )
Boilerplate	Excellent	Good
Reasoning	Strong	Moderate
Multi-file context	Better	Limited
Cost	$10–19/mo	FREE
Privacy	External servers	Your machine

Architecture (Mental Model)

┌─────────────────────────────────────────────┐
│              YOUR MACHINE                    │
│                                              │
│  ┌─────────────────┐                         │
│  │  Docker Models  │  ← qwen2.5:7B-Q4_0     │
│  │  (AI Brain) 🧠  │    runs on your GPU     │
│  └────────┬────────┘                         │
│           │ exposes                          │
│           ▼                                  │
│  ┌─────────────────┐                         │
│  │  localhost:12434│  ← OpenAI-compatible    │
│  │  REST API  🔌   │    just like -p 8080    │
│  └────────┬────────┘                         │
│           │ connects to                      │
│           ▼                                  │
│  ┌─────────────────┐                         │
│  │  VS Code        │  ← Continue.dev         │
│  │  (Your IDE) 💻  │    extension            │
│  └─────────────────┘                         │
└─────────────────────────────────────────────┘

No internet. No API costs. No data leaks.

Where This Falls Short

Be honest with yourself:

Not as smart as GPT-4-level models
Limited context window (struggles with large codebases)
Needs decent hardware for best results
Setup takes 30–60 minutes vs just paying for Copilot

When You Should NOT Use This

Working on large enterprise codebases
Need best-in-class reasoning (GPT-4 level)
Want zero setup / plug-and-play
Low-end hardware (<16GB RAM, no GPU)

Troubleshooting

Issue	Cause	Fix
`Connection error`	TCP not enabled	Docker Desktop → Settings → AI → Enable host-side TCP
Slow responses (>2s)	GPU not enabled	Docker Desktop → Settings → AI → Enable GPU-backed inference
`npx` script error	PowerShell policy	Run `Set-ExecutionPolicy RemoteSigned` as Admin
Model not showing	Not pulled	Docker Desktop → Models → Pull qwen2.5:7B-Q4_0

What’s Next?

This is just the foundation.

Docker’s MCP Toolkit can let your local AI actually act — read your codebase, modify files, understand requirements. That’s a full agent setup, and I’ll cover it in Part 2.

Final Thoughts

This setup won’t replace Copilot for everyone.

But if you care about privacy, cost, and full control over your tools — it’s absolutely worth the 30 minutes to set up.

If you’re running this setup (or planning to), I’d love to hear:
What hardware are you using?

Let’s compare setups

Check out my knowledge vault where I document everything I learn hands-on:
https://github.com/Riju007/dev-knowledge-vault

March 2026 | Docker Desktop AI features