Quick Summary
This article walks through a real local LLM vibe coding setup we configured for a healthcare client who could not send their codebase anywhere near a cloud-based AI tool. We cover what they had already tried, why it fell apart the moment real development started, how we rebuilt the environment model by model, and the workflow we left behind so it kept working long after we were gone.
Table of Contents
Local LLM vibe coding has gone from a weekend project for hobbyists to something serious teams are actually considering for daily development work. The pitch is simple: no API costs, no data leaving your machine, no waiting on a cloud provider’s uptime. For industries handling sensitive data, it can feel like the only realistic option.
But there is a wide gap between “I got a model running on my laptop” and “my team builds real features on this every day.” Most local setups are copied straight from a tutorial, tested with one small prompt, and left there. They work fine for a five-minute demo. They fall apart the moment someone tries to use them on an actual project with dozens of files, real history, and real deadlines.
This article walks through what happened when a client’s local setup hit exactly that wall, and what it took to turn it into something their team could actually rely on.
The client builds software for clinics, the kind of product where every screen touches patient names, appointment history, and insurance details. Their compliance team had already drawn a hard line: no code, no prompts, and no snippets of their database schema could go to any third-party AI service, cloud-based or otherwise. That ruled out Claude, Copilot, Cursor’s cloud features, basically anything that sends a request off the machine.
Their developers had already tried to solve this themselves. A senior engineer had set up Ollama on his workstation, pulled a small model, and wired it into a VS Code extension over a weekend. As a first attempt at local LLM vibe coding, it worked in the sense that it answered prompts. But within a couple of weeks, most of the team had quietly stopped using it.
Here is what that first setup actually looked like in daily use, once the novelty wore off and people tried to use it for actual tickets:
Running another round of prompts on top of this would not have fixed any of it. The problem was not the prompts. It was the configuration underneath them, and that is a different kind of work entirely. They came to us with a clear ask: keep everything local, keep it HIPAA-compliant, and make vibe coding with a local LLM actually usable for a team of developers working on a real codebase every day.
Before touching anything, we spent a day just using their existing setup the way their developers did. The failures were consistent, and every one of them traced back to the same root cause: a configuration built for a quick test, not for production work.
The model itself was too small for the work being asked of it. A 1.5B parameter model is genuinely impressive for its size, but asking it to reason about a multi-file React and Node codebase is asking it to do a job it was never sized for. It would generate plausible-looking code with no real grounding in what the project actually contained.
The IDE integration had no real connection to the codebase. It was configured to send the model whatever was in the currently open file, nothing more. So every suggestion was made in isolation, with no idea what other files existed, what naming conventions the project used, or what had already been built.
The autocomplete settings were untouched defaults, tuned for a fast cloud API, not for a model running on local hardware. The delay before a suggestion appeared was longer than most developers’ typing speed, so the suggestions arrived after the moment they would have been useful.
And there was no rules file or prompt structure at all. Every developer was typing prompts however they felt like it, with wildly inconsistent results depending on how the request happened to be phrased.
None of this is unusual. It is the default result of following a basic Ollama tutorial and stopping there. For most teams, local LLM vibe coding can absolutely work, but “it runs” and “it is part of the workflow” are two very different states, and almost nobody configures past the first one.
We approached this the same way we approach any vibe coding services engagement: figure out what the team actually needed day to day, then build the local LLM vibe coding setup around that, instead of around whatever model happened to be popular on a forum that week.
Getting vibe coding with a local LLM to feel reliable starts here. The team’s workstations had decent but not exceptional GPUs, mostly in the 12 to 16GB VRAM range. Rather than chase the largest model that would technically fit, we split the work by task type. For inline autocomplete and small edits, we used a smaller, fast coding model that responded quickly enough to keep up with typing. For larger feature work and multi-file changes, we configured a separate, larger coding-focused model that the team could switch to deliberately when a task called for it.
This single change, having two models configured for two different jobs instead of one model trying to do everything, fixed most of the speed complaints on its own.
Our LLM developers reconfigured the IDE integration so the model could actually see relevant parts of the project, not just the open file. Developers were shown how to reference specific files and pull in broader project context when a task needed it, instead of relying on the model to guess.
We adjusted the autocomplete delay settings to match the actual response speed of the smaller model on their hardware. Suggestions that used to arrive too late now arrived right when a developer paused to think, which is the entire point of autocomplete.
This was the part that made the biggest difference, and it is the part most setups skip entirely. Smaller local models do not carry context the way larger cloud models do. They need to be told the same things, every time, in the same place, because they will not infer it on their own no matter how many times it has come up before. We wrote a short rules file that the model reads on every prompt, covering things like coding conventions, what frameworks the project uses, and a standing instruction not to remove existing logic during a refactor without being told to.
| Task Type | Model Used | VRAM Needed | Why |
|---|---|---|---|
| Inline autocomplete, small edits | Architecture-level questions | ~6 to 8GB | Speed matters more than depth here; slow autocomplete gets ignored |
| Multi-file features, refactors | Larger coding-focused model | ~14 to 16GB | Needs more context and reasoning to handle related files correctly |
| Architecture-level questions | Larger model, used deliberately | ~14 to 16GB | Lower frequency, higher stakes, worth the slower response |
We were upfront with the client about this from the start, and it is worth being upfront here too. Local models running on workstation-grade hardware are not a like-for-like replacement for the largest cloud models, and pretending otherwise sets a team up for frustration.
Where local setups still struggle, even after reconfiguration:
For teams building toward vibe coding for MVP development, the takeaway is simple: local LLM vibe coding is a strong fit for day-to-day feature work, but it is not a substitute for someone reviewing security-sensitive logic.
Fixing the immediate friction was the short-term job. The longer-term one was making sure the team could keep this running, extend it to new machines, and not slowly drift back into the state we found it in.
We left behind a short setup guide covering the exact model and configuration choices for their hardware, written so a new developer’s machine could be set up the same way in under an hour, not reverse-engineered from memory.
We also documented the rules file and the reasoning behind it, so the team could update it themselves as conventions changed, rather than treating it as something only we understood.
A few of the tools and patterns we settled on are worth naming directly, since they came up constantly during this project:
For teams in similar positions, especially anyone weighing whether to hire vibe coders for a setup like this versus doing it in-house, the honest answer is that the tools themselves are not the hard part. Getting the configuration right for your actual hardware, your actual codebase, and your actual compliance requirements is where most setups go wrong, and where most of the time gets spent.
The client’s team is now using their local setup every day, on a codebase that has grown well past what their original configuration could handle. Nothing about their compliance position changed. What changed was whether the environment was actually configured for the work being done on it.
Local LLM vibe coding is not a downgrade from cloud tools, and it is not a magic fix either. It is a configuration problem, and like most configuration problems, it is invisible right up until someone tries to build something real on top of it. Done properly, it holds up. Left at tutorial defaults, it quietly pushes a team back to copying and pasting code by hand within a few weeks, which is exactly where this client started.
If your team needs an environment like this built for your hardware, your codebase, and your compliance requirements, Bacancy’s vibe coding services can get it configured properly from the start.
For a large share of day-to-day work, yes, especially for teams that cannot use cloud AI tools at all. The gap is mainly around large architectural changes and long-context reasoning, where local models on workstation hardware still lag behind the largest cloud models.
It depends on the model size you are running. Smaller coding models that handle autocomplete well can run on 6 to 8GB of VRAM. Larger models suited to multi-file feature work generally need 14GB or more. Matching the model to your hardware, rather than chasing the biggest model possible, matters more than the raw specs.
Running a model entirely on local hardware, with no data sent to an external API, removes the third-party data exposure that most compliance teams are worried about. It does not automatically make the rest of your application compliant that still depends on how the app itself handles data, access, and storage.
Yes. A local model generating both the code and its own test does not remove the need for someone to check what happens with bad input, missing permissions, or edge cases the model was never asked about. This is the same gap that shows up in cloud-generated code, and it does not close on its own just because the model is running locally. Treat the output as a draft that still needs a second look, not a finished feature.