In April 2026, I cancelled my GitHub Copilot subscription and switched to locally-running models for code completion. Three months later, I am still using local models as my primary autocomplete engine. But the journey was rougher than I expected, the final setup looks nothing like what I started with, and the reasons I stayed are different from the reasons I switched.
This is the full story: the bad first week, the gradual improvement, the unexpected wins, and the honest tradeoffs you should know about before trying this yourself.
Why I Made the Switch
Three factors pushed me to try local models:
Privacy. I work with client code. Some of it is under NDA. Some of it involves proprietary algorithms. Sending every keystroke to GitHub’s servers (even with their data retention policies) made me uncomfortable. Not paranoid-uncomfortable, but “I should probably not be doing this with client code” uncomfortable. One client specifically asked whether we use AI tools on their codebase, and I did not love my answer.
Cost across a team. $10/month per seat seems cheap for one person. But when you are running a team of eight, that is $960/year for autocomplete. Local models run on hardware you already own. The marginal cost per additional developer is zero.
Curiosity. The local model ecosystem in early 2026 was exploding. Qwen 3.6, Codestral, StarCoder2, DeepSeek Coder. I wanted to know how close they were to Copilot in practice, not just on benchmarks.
The Initial Setup
I started with the simplest possible configuration:
- Ollama running locally (handles model management and inference)
- Qwen 3.6-27B as the code completion model (best open model at the time for its size)
- Continue.dev as the VS Code extension (provides the IDE integration layer)
Hardware: M3 MacBook Pro with 36GB RAM. This matters because the 27B parameter model needs about 16GB of RAM when loaded, plus whatever your OS and IDE need. If you have less than 32GB total, you will want a smaller model.
Total cost of this setup: $0 in ongoing fees. You already have the computer. The software is all open source.
Week 1: Painful Honesty
I will not sugarcoat this. The first week was bad. Not “slightly worse than Copilot” bad. Noticeably, frustratingly worse.
Completion speed was the first shock. Copilot suggestions appear almost instantly because they run on massive GPU clusters. Local inference on my laptop took 200-400ms per suggestion. That does not sound like much, but when you are used to instant completions, that quarter-second delay breaks your flow. You finish typing before the suggestion appears, making it useless.
Accuracy was the second problem. Copilot has been fine-tuned on millions of code completions with user acceptance/rejection signals. It knows what developers actually want. Qwen 3.6 is a general-purpose model that happens to be good at code. It completes correctly but not always relevantly. It would suggest syntactically valid code that did not match the patterns in my project.
Context awareness was almost nonexistent. Copilot understands your open tabs, your recent edits, and the general structure of your project. The basic Ollama + Continue.dev setup only sees the current file. It would suggest imports from packages I had never used, or function calls with incorrect parameter names because it did not know my codebase.
I almost gave up on day three. What kept me going was stubbornness and the fact that I had already cancelled Copilot and wanted to give it a fair trial.
Week 2: Tuning and Improving
The breakthrough was realizing that “install model and go” is not enough. Local models need configuration that cloud tools handle for you.
System prompts made a huge difference. I added a Continue.dev system prompt that said: “You are completing code in a TypeScript project using Express, Prisma, and React. Prefer async/await. Use our existing utility functions when possible. Do not suggest external packages without being asked.” This alone eliminated maybe 40% of the irrelevant suggestions.
Context feeding was the next improvement. Continue.dev supports loading additional context files. I configured it to always include my tsconfig.json, my main types file, and a custom instructions file that described our coding conventions. This got the model to suggest code that actually matched our patterns.
Model quantization helped with speed. I switched from the full 27B model to a Q5_K_M quantized version. Slightly less accurate, but completion latency dropped from 300ms to about 150ms. At 150ms, suggestions feel responsive enough to use without frustration.
After these changes, I was getting maybe 70% of Copilot’s quality. Not great, but usable. The suggestions were relevant more often than not, and the speed was acceptable.
Month 1: Adding Codestral (Game Changer)
The biggest single improvement came when I added Mistral’s Codestral model specifically for autocomplete. This model was designed from the ground up for code completion. It is smaller (22B parameters), faster to infer, and dramatically better at the specific task of completing a line or block of code.
I kept Qwen 3.6-27B for chat and explanations (where you need more reasoning) and switched to Codestral for the actual inline completions. This split-model approach gave me:
- Fast completions (100-150ms)
- Higher acceptance rate on suggestions
- Better “finishes your sentence” behavior
- Less hallucination of non-existent APIs
At this point I was at maybe 80% of Copilot quality for routine code completion. The gap was most noticeable when working in unfamiliar libraries (Copilot’s training data advantage shows there) and when needing multi-line completions (Copilot is better at predicting your next 5 lines, local models usually give you 1-2 lines at a time).
For anyone looking to set up something similar, my local-first AI developer stack article covers the full configuration.
Month 2: Finding the Rhythm
By the second month, I stopped thinking about local models vs Copilot. It was just my setup. The completions were there, they were helpful more often than not, and the privacy benefit was constant background peace of mind.
Things I noticed during this period:
I wrote more code manually than before. Without the constant stream of multi-line suggestions, I actually typed more. This is not necessarily bad. I was more intentional about what I wrote. Less “accept whatever the AI suggests,” more “write what I mean.”
My muscle memory adapted. I stopped expecting instant completions and developed a rhythm where I would start typing, pause briefly at the end of a line, and accept or reject the suggestion. Different from the Copilot flow (where you might just press Tab every few seconds) but perfectly workable.
Context matters more with local models. Copilot can be lazy because it is fast and cheap. If a suggestion is wrong, you just wait for another one. With local models, you want each suggestion to count. So I got better at structuring my code in ways that gave the model clear context: good variable names, explicit types, clear function signatures.
I was spending $0/month on local autocomplete. For anything complex (multi-file refactoring, debugging hard problems), I used Claude Code on an as-needed basis, spending maybe $20-50/month depending on how much complex work I had that week. Total monthly cost: $20-50 vs the previous $10 for Copilot alone (which could not do complex tasks at all).
Month 3: Would Not Go Back
Three months in, here is my honest assessment:
Completion quality: 80% of Copilot for routine code. 90% if I am working in a well-structured project with good type definitions. 60% if I am working in a new library or framework I have never used (this is where Copilot’s training data shines).
Speed: Acceptable. Not instant, but fast enough that it does not break flow. The 100-150ms latency with Codestral is something I no longer notice.
Privacy: Perfect. Nothing leaves my machine for autocomplete. Client code stays client code. This alone is worth the quality tradeoff for my situation.
Reliability: Better than cloud in some ways. No rate limits. No outages. No “Copilot is slow today” moments. The model runs when my laptop is on. Period.
Cost: $0 for the autocomplete itself. I spend on cloud AI for complex tasks, but that is a different tool for a different job.
What I Still Use Cloud AI For
Local models are great for autocomplete and simple chat. They are not great for:
Complex multi-file refactoring: This is where Claude Code with Opus 5 earns its keep. No local model can hold 20 files in context and make coordinated changes across all of them.
Debugging complex issues: When I need deep reasoning about why something fails, I want Opus 5. Local models give me surface-level suggestions. The cloud model actually traces the logic.
Learning new libraries: When I am working with an unfamiliar API, cloud models are significantly better because they have more extensive training data on recent libraries.
My cloud AI spend averages $35/month. Some months more (during complex sprints), some months less (during maintenance periods). I track this using the approaches in my API pricing comparison and cost reduction guide.
The Full Cost Breakdown
| Item | Copilot Setup (Before) | Local Setup (After) |
|---|---|---|
| Autocomplete | $10/mo (Copilot) | $0 (Ollama + Codestral) |
| Complex tasks | $0 (Copilot could not do this) | ~$35/mo (Claude Code as needed) |
| Hardware | Already owned | Already owned |
| Total | $10/mo | ~$35/mo |
Wait. It is more expensive? Yes. But the comparison is misleading.
With Copilot at $10/month, I got autocomplete only. No complex refactoring, no multi-file editing, no agentic tasks. When I needed those, I either did them manually (hours of work) or paid for a separate tool on top.
With my current local setup at $35/month average, I get autocomplete (local, private, reliable) PLUS complex AI-assisted development on demand. It is not replacing like-for-like. It is replacing Copilot AND adding capabilities I did not have before.
If you purely want to replace Copilot with local models at lower cost, you can do it for $0. Check my zero-dollar AI coding stack guide. But you will sacrifice quality. The honest truth: local models are about 80% as good as Copilot for autocomplete. Whether that 20% matters depends on you.
Who Should Try This
You should try local models if: you work with client code under NDA, you want zero external dependencies for basic AI features, you have a machine with 32GB+ RAM, or you are on a team where the per-seat Copilot cost adds up.
You should stay on Copilot if: you work exclusively on open-source or internal code with no privacy concerns, you value the last 20% of completion quality, you are on a less powerful machine, or you just do not want to tinker with setup.
The middle ground: Use local models for autocomplete (private, free) and cloud AI for complex tasks (paid, powerful). This is what I do, and it gives you the best of both worlds. I describe this hybrid approach in detail in my fifty-dollar AI coding stack article.
My Final Setup (July 2026)
- Autocomplete: Codestral via Ollama, connected through Continue.dev in VS Code
- Chat/explanations: Qwen 3.6-27B via Ollama, also through Continue.dev
- Complex tasks: Claude Code (Max plan, used as needed)
- Quick debugging: DeepSeek V4 Flash via API for cheap, fast answers
- Editor: VS Code with Continue.dev extension
Everything runs locally except when I explicitly choose to use a cloud model for a complex task. My keystrokes, my code, my patterns stay on my machine by default. Cloud is opt-in, not opt-out.
For the full walkthrough of running Qwen locally or setting up a complete local-first stack, I have detailed guides covering the exact commands and configuration files.
FAQ
What hardware do you need to run local models?
Minimum: 16GB RAM for a 7B parameter model (basic quality). Recommended: 32GB RAM for a 22-27B model (good quality). Ideal: 64GB RAM or a machine with a dedicated GPU for the fastest inference. Apple Silicon Macs are particularly good at this because of unified memory. I run everything on an M3 MacBook Pro with 36GB.
How do local models compare to Copilot for languages other than TypeScript?
My experience is primarily TypeScript, Python, and Go. For TypeScript and Python, local models are very good (80% of Copilot). For Go, slightly worse (maybe 70%) because the training data skews toward Python and JavaScript. For less common languages (Rust, Elixir, Kotlin), the gap is larger. Copilot’s training data advantage matters more for languages with less open-source training data.
Can you use local models in a team setting?
Yes. You can run Ollama on a shared server and have multiple developers connect to it. This centralizes the GPU cost and means individual machines do not need heavy RAM. We tested this briefly with three developers and it worked, though you need to manage concurrent request queueing.
Does the quality gap with Copilot narrow over time?
Yes, noticeably. Qwen 3.6 (released early 2026) was significantly better than Qwen 2.5 (late 2025). Each new model generation closes the gap by a few percentage points. I expect local models to reach 90%+ of Copilot quality within a year.
What about battery life impact?
Running inference locally does drain your battery faster. I lose about 20-30% more battery during a work session compared to when I used Copilot. Plugged in, this does not matter. On battery, I sometimes disable local autocomplete for light tasks and only enable it when I am actively coding. It is a real tradeoff for laptop users who work unplugged frequently.