01What Local AI Models Are and How They Work

Local AI models are AI systems that run on your own computer or server instead of on cloud servers. They work by using your device's processing power to handle tasks like coding, reasoning, and chatbot responses. This means you do not need an internet connection or a paid API to use them.

In this episode, Julian explains how to configure these models within an agentic operating system (Agent OS). The Agent OS is a software platform that helps you build and run AI agents. It includes a special section for local models, where you can choose from different local options and test them directly.

Julian notes that local models are not always the best choice. They can be slow and less intelligent than frontier models. But they are useful when you want privacy or have strong hardware.

For example, a Nvidia RTX 5090 graphics card makes local models run much better than on a basic Mac Studio.

02Three Ways to Use Local Models with Agent OS

Agent OS gives you three main ways to use local AI models. First, you can use the built-in Local System section. This is the simplest method: you go to that section, pick a model, and start using it right away.

  • Local Model Section: This is the dedicated area for local models. You can configure and preview your built agents there.
  • Free AI Coder Section: This is for free cloud models, but you can also switch to local if you want. It includes options like Omniroot and KNIME router.
  • Plug into Any System: You can attach a local model to any agent system in Agent OS, such as Hermes or Free Cored Code.

Julian demonstrates these options in the episode. He shows how to preview your work and access your workspace from the local model section. This makes it easy to manage all your projects in one place.

If you prefer free cloud models instead, the Free AI Coder section is a good alternative. Julian mentions that Omniroot and KNIME router are available there. This is perfect if you want to avoid local slowdowns.

03How to Set Up a Local Model in Agent OS

Getting started with a local model in Agent OS is straightforward. Julian walks through the process, and here are the steps he shows:

  1. Open your Agent OS interface and find the Local Model section.
  2. Click on that section to see available local models, such as Maple Reasoning.
  3. Select the model you want to use. You can adjust settings if needed.
  4. Once selected, you can start interacting with the model directly in that section.
  5. To test your work, click the Preview button to see how your agent behaves.
  6. Access your Workspace to see all past projects and agents you have built.

Julian also mentions that you can type "local" in the search bar within the AI Profit Boardroom community. This brings up many training videos and guides on using local models.

If you run into issues, you can ask questions in the community. Julian says he is happy to help personally, and members can get real-time support.

04How to Use Local Models in Day-to-Day Tasks

Using local models in Agent OS is similar to using any other AI model. You can assign it to different agents, like coding tools or assistant bots. For example, Julian mentions plugging a local model into Free Cored Code, which is a coding agent.

He previously used a model called LFM 2.5 on his Mac Studio. It was fast but not very smart. Still, it worked well for simple tasks.

You can do the same: choose a local model that matches your needs, whether for speed or intelligence.

To switch between models, just go to the settings of the specific agent you are using. You can select a local model from the dropdown. Julian shows that this works for all systems, including OpenClaw and Hermes.

Always test your setup before relying on it. Use the preview feature to see how the agent responds. This helps you catch errors early and adjust the model or settings.

05Results and Comparisons: Local vs Free Cloud Models

In the episode, Julian shares his honest take on local models. He says, "I don't find that local models are that great when I'm using them on a MacStudio." He sees them as slow and less capable than frontier models. Frontier models are top-tier cloud AI like GPT-4 or Claude.

Julian compares local models to free cloud options like Omniroot and KNIME router. These free models often perform better because they run on powerful servers. For most users, free cloud models are a smarter choice.

However, with powerful hardware like an RTX 5090 or DGX Spark, local models become more practical. Those setups can run larger models faster, making local AI usable for serious work.

Julian also notes that some local models, like LFM 2.5, are fast but not very intelligent. That trade-off matters. If you need speed and privacy over top performance, local might be fine.

But if you want the best results, use cloud models.

06Tips, Limits, and Mistakes to Avoid

Julian offers several tips for using local AI models with Agent OS. The biggest tip is to know your hardware. If you have a Mac Studio, local models may disappoint.

But with an RTX 5090, they work well.

Here are other important tips and warnings:

  • Do not expect top-tier intelligence from local models. They often lag behind frontier models.
  • Test before committing. Use the preview feature to see if the model fits your task.
  • Consider free cloud models if speed is a problem. Omniroot and KNIME router are great alternatives.
  • Plug local models into specific agents only when needed, like for coding with Free Cored Code.
  • Watch out for slow performance. Local models on weak hardware can be painfully slow.

Julian also warns that local models are "not that great" for most people. He rarely talks about them because they are not practical for everyday use unless you have strong gear.

One mistake is ignoring the free AI coder option. If your goal is to save money, free cloud models achieve that without sacrificing speed. So think about your actual needs before choosing local.

07Who Should Use Local Models and Why It Matters

Local AI models are best for users with powerful hardware. If you own a DGX Spark or an RTX 5090 graphics card, local models can be a great option. These systems can handle the heavy computation that local AI needs.

Why does this matter? Local models give you privacy and offline access. Your data never leaves your machine.

This is valuable for businesses with sensitive info or for developers who work without internet.

Julian explains that for most people, local models are not worth the trouble. But for those with the right setup, they offer a free and secure way to run AI. You avoid API costs and keep your data local.

If you are a beginner, start with free cloud models. They are easier and faster. As you grow and get better hardware, you can experiment with local models later.

Either way, Agent OS makes it simple to switch between both.

In the end, the choice depends on your hardware, budget, and need for privacy. Test different models and see what works best for you.