Vibe Coding and the Rise of AI-Assisted Development

Vibe coding—using LLMs to support code creation or even generate code directly—is gaining traction fast, and it’s easy to see why. By automating simpler parts of the engineering workflow, vibe coding can dramatically reduce time spent on development. More and more, we’re also seeing full projects produced through a sequence of well-crafted prompts to an LLM.

Powering this shift is the continuously expanding GPU cloud. Whether you rely on a proprietary service like ChatGPT or Anthropic, or you run your own LLM in the cloud through platforms such as centron’s GPU infrastructure, these models depend on strong GPU compute to operate. These systems are the engines driving the AI coding revolution happening right now.

But what happens when the GPU cloud isn’t available?

In this tutorial, we offer one practical answer to that challenge. Follow along as we cover standout options for local LLMs, the kinds of local machines that can run them, and offline vibe-coding techniques you likely won’t find elsewhere.

Key Takeaways

Your local hardware determines which model sizes you can realistically run. We suggest having at least 16 GB of accessible RAM to follow this tutorial comfortably.

Local, agentic vibe coding is now achievable thanks to compact “thinking” models like Qwen3 2507 and Nemotron Nano v2.

Getting started is simple on any operating system using local tools such as LM Studio and Ollama.

The Best Local Agentic LLMs in September, 2025

If you want to begin local agentic coding, there are many models you can choose from. That’s a benefit—but also a drawback—because it can be tricky to determine which options are actually worth your time. Many model families have multiple releases and sizes, and matching the right model to your device adds another layer of complexity. For our testing, we used a 2021 MacBook Pro with an M1 Pro chip and 16 GB of memory. Let’s review some of the strongest local agentic LLMs available right now, and which ones make the most sense to use.

Qwen3 2507

In our view, Qwen3 is the best starting point for local agentic modeling. This model family ranks among the most capable across a wide range of benchmark categories, and it also performs exceptionally well in agentic scenarios. Both the thinking and instruct versions are very strong, and the 2507 releases improve even further on the original line.

We strongly recommend Qwen3 2507 as the top first-choice for local agentic modeling and vibe coding. Our machine can only manage the 8b version, but the 30b-a3b mixture-of-experts model is noticeably stronger. Based on our hands-on experience, Qwen3 was also the smoothest to incorporate as a coding assistant within our workflow.

Nemotron Nano v2

NVIDIA’s Nemotron Nano v2 is another excellent pick for agentic work. Offered in 9b and 12b sizes, these models are among our favorites for code optimization, code editing, and vibe coding. NVIDIA trained this suite entirely from scratch using the Nemotron-H architecture. Because it is a unified model for both reasoning and non-reasoning tasks, it addresses requests by first producing a reasoning trace and then delivering a final answer.

In our tests, we were impressed by how well it worked with the tools available inside the IDEs we tried. It delivered results similar to the Qwen3 2507 8b model, and it can operate within the comparatively limited VRAM available on the M1 Pro.

GPT-OSS

One of the strongest open-source models for local developers available today is GPT-OSS from OpenAI. We recommend it to anyone who has at least 24 GB of VRAM on an NVIDIA or AMD consumer GPU. The GPT-OSS 20b variant, especially, was trained with GPUs of that class in mind.

GPT-OSS is a powerful agentic model for programming. It performs extremely well for tool usage, few-shot function calling, chain-of-thought reasoning, and the medical HealthBench benchmark (even surpassing proprietary models such as OpenAI o1 and GPT-4o).

Hosting the Models Locally

There are plenty of ways to run local models, depending on what you need. In this section, we share our two preferred approaches for hosting LLMs on local hardware. Our favorite hosting tools are LM Studio and Ollama.

Follow along for guidance on getting started and deciding which option fits you best.

LM Studio

The first tool we want to highlight is LM Studio, an application built to let you run models like gpt-oss, Qwen, Gemma, DeepSeek, and many others locally—private and free. LM Studio has a clean, friendly interface and makes it straightforward to choose, download, and run language models from inside the app, including support for both GGUF and MLX model formats. It also includes a wide range of integrations and custom build features that can expand what you can do, including RAG.

We especially recommend LM Studio for Mac users, since they can take strong advantage of the overall experience.

Ollama

Another project we’re big fans of is Ollama. It was one of the early projects to branch from the open-source Llama.cpp ecosystem, and it remains one of the most widely used LLM services in the open-source community. We like its command-line workflow, which makes downloading, interacting with, and organizing models as painless as possible. Like LM Studio, it can also run a server so you can host a model endpoint.

Ollama is an excellent fit for Linux users because it makes model interaction through the terminal simple and direct.

Coding with Local Large Language Models

Developing with LLM assistance—often referred to as vibe coding—is becoming more widespread and more practical as these models grow more capable. However, doing it effectively without access to either a strong desktop GPU or cloud resources can be challenging. With this tutorial, our goal is to show how the models and tools discussed earlier can be paired with local hardware to successfully vibe code offline.

VS Code Continue

Our preferred method for offline vibe coding with local models is VS Code Continue, an integration for the widely used IDE that makes it easy to bring agentic LLMs into your coding process. With VS Code Continue, you can connect to the endpoints provided by LM Studio or Ollama and work directly with your local files.

To begin, install Ollama or LM Studio along with Visual Studio Code. After that, once you’ve downloaded the model you want into the hosting tool you chose, open the VS Code extensions marketplace, search for Continue, and install it.

After installation, you can open the extension from the left sidebar using the Continue logo. From there, you can configure the chat agent to detect models available via LM Studio and Ollama, allowing you to switch between models hosted in either tool without friction.

Continue includes three default templates for interacting with the agent: Agent, Plan, and Chat. The first two include built-in tool access for working with files, with Agent generally being stronger for file edits. Chat is focused on conversation with context. We had strong results using the modes in a targeted way: Chat to discuss content, Plan to outline modifications, and Agent to apply changes automatically.

In practice, Continue’s constraints are mostly the constraints of whichever model you’re using. As model capabilities progress, tool usage should improve significantly. We were genuinely impressed by how well all three models improved our code and automated smaller workflows while we were offline. We recommend Continue for anyone already comfortable with VS Code and related forks like Cursor—especially on Mac or Windows systems.

Zed

Our second favorite offline vibe-coding IDE is Zed. Zed is a free, open-source editor for Linux and macOS created by Zed Industries, designed for coding with language model assistance. It’s a highly capable option for editing and automation work.

To get started with Zed, download the application from its website and install it. Once installed, open it and use the file bar to load a local directory you want to edit.

Inside the IDE, you can chat with your LM Studio or Ollama-hosted models by clicking the second-to-last icon at the bottom-right of the window. Choose the model you want before proceeding. After selecting it, you can decide which profile to use: Write, Ask, or Minimal.

Like Continue’s templates, these profiles provide different levels of tool access. Write is used for applying changes to files via prompts, Ask is geared toward questions about the files, and Minimal is primarily for chatting. If you want additional tools and integrations, you can build those workflows around these profiles.

Based on our experience, Zed is a fantastic tool for this kind of development. It includes strong built-in features that make it very easy to code with LLM support, including editing and even writing new code. We recommend Zed for Mac and Linux users who need a capable setup while traveling or working on the go.

Conclusion

To wrap up, local development is starting to shift dramatically thanks to the availability of edge models and the growing ecosystem of applications that make using them easier than ever. In this article, we covered our preferred models for editing code, two excellent tools for hosting those models locally, and our favorite IDE integrations that rely on these services to enable offline vibe coding. We encourage you to try each option and see which one best matches how you like to work.