I finally found a local coding LLM that I actually want to use - How-To Geek

If you run an AI locally, you get complete privacy, no API or subscription costs, offline access, and you never have to worry about running into your usage limit right when you're in the middle of something. For a long time, AI coding assistants were janky, unreliable, and painfully slow, but newer local models can hold their own against the cloud-based models as long as you're careful about how you use them.

Qwen's latest coding model release has been especially impressive, and I now use the local model about half the time.

Qwen 3.6-27b:q3 makes local vibe coding viable

Pair it with VSCodium for a fully open-source experience

There are a ton of local AI models that can help you code locally, and they frequently leapfrog each other as models improve over time. For the most part, I haven't found these very impressive—they're good as very fancy autocompletes, but not much else.

However, recent models make them much more appealing. I've been using some of Qwen’s coding-specific models, and found they are finally in a position where they're usable on moderate hardware and practically useful. It handles code completion, refactoring, and writing tests, and it does it pretty well. You can use it to plan or directly write, though I'd strongly recommend planning first. It isn't nearly as smart as the big cloud models, and it needs the help.

I run my local coding models in VSCodium via the Cline extension. The entire thing appears as a small sidebar where you type your commands, approve code snippets, and manage your context window. I mostly use my local coding AI for simple things, while I leave more complex jobs or refactoring to Claude to save tokens.

Because the entire setup relies on Ollama, I can also make my local AI accessible to any device on my home network. That means I'm not stuck seated in front of my desktop—I can take my laptop

Cloud models are better, but local is inexpensive and private

Privacy, cost, and availability add up

Even if a cloud model is more "intelligent," you should still consider a local setup. The most pressing issue is privacy and security. When you run a model locally, your code never leaves your machine. If you're handling proprietary company data or sensitive client information, that is very important.

Cost also matters. Claude, ChatGPT, Gemini, and all of the other major players charge monthly for access. Those plans start at about $20 per month, but the costs can grow explosively if you're not careful. A viable local agent means you can stop paying monthly subscriptions or worrying about per-token fees.

Once you own the GPU, your only ongoing cost is the electricity. It sounds like a dubious value proposition at first, but consider that Claude Max costs at least $100, which is the minimum subscription someone doing a lot of coding will need. After a year, that is an RTX 5080. After two years, that is an RTX 5090 (if you can find one at MSRP).

It is also nice not to depend on anyone else's servers. On more than one occasion, I've gone to use Claude or Codex to write some code, only to find the servers are temporarily down. With your own local setup, your downtime is mostly under your control.

Running a coding LLM locally has some tradeoffs

VRAM, quantization, and context are the real constraints

Running a local coding LLM isn't without its drawbacks, however. The big limit is hardware.

If you're running a mid-range consumer GPU, like the 5070 Ti I use, you're going to run into bottlenecks. The primary constraint is VRAM, which dictates both the size of the model you can load and the length of the context window you can maintain.

This is where quantization comes in. You'll see terms like Q4, Q5, or Q8. That is basically an indicator of how compressed the model is. While a Q8 (8-bit) model is more precise, a Q4 (4-bit) model allows you to run a larger model on hardware with less VRAM with only a slight decrease to output quality. With the right quantization, I can use some 27B parameter models on my 5070Ti, though larger models are out of reach.

You should also expect a speed difference between local models and cloud-based models. Local models will struggle to fit large, complex jobs into the context window.

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Local coding LLMs are finally worth using

The local LLM offerings have finally crossed the line from a fun novelty to something I can actually use daily. A big part of making these models useful is integration.

You should try setting this up as a supplement to your cloud tools by attaching it to VSCodium or the IDE of your choice. It might not replace the most powerful models for every single task, but having a private, free, and always-available assistant on your own hardware is a great addition to any dev environment.