Why I Switched from Copilot to LlamaVim
TL;DR
I got a new beefy Mac studio M3 Ultra with 96GB of RAM, and it's a beautiful machine. I knew that it would be great, specifically for running and developing with local LLMs, so I decided to try something new: LlamaVim. It’s a new open-source plugin from the same team behind Llama.cpp, and it’s designed to integrate self-hosted models directly into your Neovim or Vim environment. There’s even a VS Code version.
I have been using the Neovim Copilot plugin since it came out. It has been a big value add for tests and error handling in my Go code, but lately it has been kind of a letdown ever since Claude Sonnet came out. This post is about why I switched from GitHub Copilot to LlamaVim, and why I’m probably never going back.
Why LlamaVim?
Copilot works fine, but it has one flaw for me: latency. It's never instant. Because it makes API calls to OpenAI, there’s always a bit of delay before suggestions appear, and that’s a problem when I’m deep in a flow state. I can use Claude to generate most of my boilerplate code, so when I am typing, I need the suggestions to be more like the LSP; just fast and effective to make sure I remember to have contracts in place so I can surgically make changes to business logic and fix bugs.
LlamaVim does make API calls, but since I run it locally on my Mac Studio with 96GB of unified RAM, there’s no API call. The result? Blazing fast autocomplete. It’s not trying to replace my brain, t’s just giving me boilerplate and docstrings faster than I can type them.
Simple Setup
Installing LlamaVim was a dream. Just a one-line change to my LazyVim config and I was good to go:
{
'ggml-org/llama.vim',
}
You do need to run a local server (LlamaServer), either in a background tab or as a persistent service. This binary is installed with llama.cpp and so is the model. So you just have to run this simple command and once it’s up, it just works:
llama-server --fim-qwen-7b-default
Choosing the Right Model
You can use any GGUF model with LlamaVim, but they recommend a FIM (Fill-In-the-Middle) model for best coding performance. That was new to me, but it makes sense: when you're editing code, you're often inserting text in the middle of lines—not just appending to the end. FIM models are trained to handle that.
I used the Qwen3-FIM-7B model. It’s relatively recent and surprisingly performant, especially when I just want snappy completions and don’t need a massive 70B parameter model.
Performance and UX
Good news: it was fast.
With Copilot, I always feel like I have to pause to give it time. And when I don’t pause, it just doesn’t show up. But LlamaVim on the other hand responds instantly.
Not to mentions the interface is so much more satisfying. It shows tokenization metrics, prompt latency, and tokens-per-second—all inline. I felt like I leveled up just by having those numbers right there in my editor.
Insert screenshot of prompt stats or metrics window here.
Compared to Copilot
Honestly, LlamaVim is just easier. It was easier to install than Copilot, faster to use, and more flexible in terms of models. Copilot always has a bit of configuration friction in Vim. LlamaVim just worked without much additional effort.
And again, this isn’t about replacing human coding. It’s about speeding up the repetitive parts—docstrings, function headers, boilerplate snippets. Stuff I already know how to write, but would rather not.
When I do need accuracy or large swaths of code, I’ll still use Claude Code, but for fast, local development LlamaVim wins every time.
What You Need to Try It
A machine with a decent amount of RAM (I used 96GB, but it ran fine on a 32GB M4 Mac too)
Neovim + LazyVim (or your preferred plugin manager)
LlamaServer running in a terminal tab or as a service
A FIM-style model like Qwen3-FIM-7B
Final Thoughts
This is one of the best dev experience upgrades I’ve had in a long time. If you’re already deep in Neovim and tired of waiting on Copilot, LlamaVim is worth the 10-minute install. And if you’re building in a home lab like I am, it’s even better—no cloud costs, no API rate limits, just fast local inference that keeps up with your brain.
Insert short stream video clip of it in action.
Final Aside: Be Careful With IP and Licensing
Before I wrap this up, there’s one more reason I advocate for local inference—DATA protection.
If you’re working with any private codebase, be careful with tools that use cloud APIs. The free and personal tiers of services like Copilot and ChatGPT do not protect your data from being used in model training. It’s all in the fine print:
Unless you're on an enterprise-tier license, there's no guarantee your data isn’t being retained, logged, or used to improve their models.
That’s why I only use cloud tools like Claude or Cloud Code with enterprise licensing, and why I default to self-hosting anything I can. If it runs locally and never leaves the machine, you don’t have to worry about compliance, privacy, or retraining risk. That’s not paranoia—that’s just good software practice.
If your project is open source? Great, use whatever. But if you're working on private IP, know the license you're under before you get into trouble.