Cloud AI vs. Local AI: What You Need to Run AI on Your PC

Cloud AI versus local AI comes down to a practical question: whose computer is doing the work? Services like ChatGPT generally process your requests on the provider's servers, while local AI runs a downloaded model on your own hardware. For everyday PC users, that choice affects privacy, cost, convenience, and what your computer needs to handle.

In our first AI guide, we explained what a large language model is. Now we will look at where those models run, why you might choose one approach over the other, and how AI agents fit into the picture.

What Is Cloud AI?

Cloud AI runs the model on computers operated by a service provider. You send a question through a website or application, their hardware processes it, and the answer comes back to your device.

This is how people generally use services such as ChatGPT, Claude, and Gemini. Your PC handles the interface and connection, while the provider handles the heavy computing. This allows even a modest laptop or even something like a Chromebook the ability to access an AI model that would be impossible or impractical to run on that laptop.

Cloud services also build tools around their models. Depending on the service and plan, these might include web searches, spreadsheet analysis, image generation, voice conversations, and connections to other applications. Downloading a language model does not automatically give you that entire toolbox out of the box, but the ability to build those yourself later.

What Is Local AI?

Local AI runs the model on your own computer. You install an application, download a compatible model for your needs, and use your PC's processor, memory, and graphics card to generate your responses.

Think of the application as the player and the model as what you load into it. Like inserting a tape into your teape deck , for you Boomers out there. Software such as LM Studio and Ollama can handle running downloaded models, but installing the application is only part of the setup.

A point of note here. You are usually downloading a model that has already been trained. You are not teaching a brand-new AI everything from scratch. (Although that is possible). Running a trained model to generate an answer is called inference, a term you will encounter in AI software and hardware descriptions.

Once the required software and model files are downloaded, local chat can work without an internet connection. LM Studio documents this in its offline operation guide. Downloads, web searches, and external services still require connectivity.

One distinction matters here: installing an AI application on your PC does not necessarily make it local AI. A desktop application can still send every request to a cloud service fif you configure it that way. What matters is where the model processes your information.

Why Run AI Locally?

Privacy is one of the strongest reasons. A fully local workflow can process your prompts and documents without sending their contents to an AI provider. That can be useful for personal journals, unpublished writing, internal notes, or other information you would rather keep on your own computer.

For example, you might want help organizing a personal journal entry or rewriting an unpublished article. A suitable local model can work with text you supply while you keep that processing on your PC.

However, check the configuration. Some applications offer both local and cloud models, along with plugins or online tools. Ollama, for example, provides a setting to disable its cloud features. Selecting a downloaded model and checking connected tools matters more than assuming everything is private because the application is installed locally.

Local AI also gives you offline access and more control over your setup. You can keep a downloaded model, experiment with alternatives, and decide when to update. An internet outage does not have to stop a writing task that only needs information already on your computer.

There is also the subscription question. Free applications and freely downloadable models can let you generate answers without a monthly AI subscription or per-request provider charges. Model licenses still vary, particularly for commercial use.

Local AI is not cost-free, though. Electricity, storage, and hardware upgrades count. Buying an expensive graphics card to avoid a subscription can take quite a while to pay for itself.

Why Choose Cloud AI Instead?

Convenience is a perfectly good reason. Cloud services handle the computing infrastructure and model deployment, and they can offer models too large to run practically on an ordinary home PC.

A smaller local model might rewrite an email or explain an Excel formula quite well, then struggle with a complicated coding problem or a lengthy document. Results depend on the model and task. Getting a model to fit into memory does not guarantee that it will do the job well.

Cloud services may also provide useful tools that would require additional setup locally. If you want an assistant that searches for current information, reads files, and works across applications, the complete service can matter as much as the model behind it.

The tradeoffs include dependence on your connection, service availability, usage limits, pricing, and the provider's data policies. Your requests leave your computer to be processed.

Those policies deserve a look rather than a blanket assumption. For example, ChatGPT provides controls over whether new conversations are used for model training, while business offerings have different defaults. Turning off training does not make the processing local.

What Do You Need to Run AI on Your PC?

You need compatible software, a downloadable model, enough memory, and storage for its files. A supported graphics card can improve performance considerably, but you do not automatically need to buy a new computer.

The following are practical starting points for experimenting with smaller language models. They are not guaranteed requirements for every application or model.

• System memory: Around 16GB of RAM is a useful starting point. With 8GB, model choices and available working space are tighter. Having 32GB provides more room for models, longer conversations, and your other applications.
• Graphics hardware: A supported GPU can speed up responses. Dedicated video memory, called VRAM, helps determine how much of the model the graphics card can hold. GPU compatibility, model size, and memory requirements all matter.
• Processor and storage: Check the application's CPU requirements and allow several gigabytes per model, sometimes considerably more. An SSD helps load model files, but storage space does not replace working memory.

RAM and VRAM are different pools of memory on a typical PC with a dedicated graphics card. Having 32GB of system RAM does not give an 8GB graphics card 32GB of VRAM. Systems with unified memory handle this differently.

Starter AI Models and Hardware Guidelines

These guidelines assume quantized models, short conversations, and enough free memory for Windows and your other applications. A dedicated GPU is optional, but a compatible one can make responses much faster.

The VRAM figures are approximate targets for GPU acceleration, not mandatory minimums. Some applications can run models on the CPU or split the work between system memory and the graphics card, although responses may be slower. RAM and dedicated VRAM are not interchangeable.

Allow additional storage for model downloads. The model file is only part of the memory requirement, and increasing the context length can substantially increase memory use. Check the selected download and your application's compatibility estimate before loading it.

Suitable software can run models using the CPU, and some configurations divide work between the CPU and GPU. That may allow a model to run when it does not fit entirely in VRAM, although speed can suffer. There is a difference between "it runs" and "I enjoy waiting for it."

Check the LM Studio system requirements or Ollama hardware support documentation before buying hardware specifically for either application. Support varies by operating system, graphics card, and driver.

You do not need an "AI PC" badge to get started. An NPU, or neural processing unit, is not a universal requirement for running a local language model.

Which Local AI Software Should You Try?

There are several offering out here now but the two most popular are LM Studio and Ollama. We suggest you start there as there a lot of documention out there on these.

LM Studio provides an interface for finding, downloading, loading, and chatting with compatible models. It is a useful starting point for readers who want to experiment through a desktop interface.

Ollama runs downloaded models and can provide a local AI service that other applications connect to. That makes it useful when you want to move beyond a standalone chat window and connect a model to other tools.

Both offer a free way to experiment with local models. Optional cloud features or external services can have separate costs, so check what you are selecting.

Start with a small instruction-tuned model intended to follow requests and hold conversations. Where available, use the application's compatibility information to help choose one that fits your hardware.

Give it ordinary work first. Ask it to rewrite a paragraph, explain an Excel formula, or summarize a short passage. Check whether the answer is correct and whether the response speed is comfortable before trying a larger model.

Where Do You Get Local AI Models?

The easiest place to start is inside the software you installed. LM Studio lets you search for and download compatible models through its interface. Ollama provides a model library with model names, available sizes, and instructions for running them.

You will also encounter Hugging Face, a major hub where developers publish models and model files. It offers far more choices, but those choices can be confusing when you are starting out. Different downloads may contain different sizes, quantization levels, or file formats, and not every file works with every application.

For your first model, use your application's built-in download tools or its recommended library. Look for an instruction-tuned or chat model that fits your available memory. Read the model description, often called a model card, for its intended uses, limitations, and license.

You do not need to download the biggest model available. Start small, see how it handles your actual tasks, and work upward if needed. Collecting 200GB of models is easy finding a place to store them is not. Finding the one you will actually use takes a little experimentation, as well.

Three Starter Models Worth Trying

You do not need to start with a massive model. In fact. smaller models give you a practical way to experiment with local AI before considering hardware upgrades and can suit your needs without needing the upgrades. However, just like software for your PC there is a huge list of modles you can try out there, with varying results. Here are three suggestions that you can start with. Note, these are at time of publishing... But you'll get the idea once you look at them

Model	Suggested System RAM	Optional GPU VRAM Target	Starting Tasks
Gemma 3 1B	8GB or more	4GB provides room for this small model	Basic questions, short rewrites, learning local AI
Qwen 3.5 4B	16GB or more	6GB to 8GB for a quantized version and modest context	Emails, summaries, explanations, basic coding questions
Qwen 3.5 9B	16GB may work; 32GB gives more breathing room	12GB or more provides useful headroom	Comparing a larger model on writing, explanations, and coding tasks

In LM Studio, search for the corresponding model and choose a compatible download. A Q4_K_M GGUF version, where available, is a reasonable starting point for balancing memory use and quality. Or download it from HuggingFace

Those RAM figures are starting guidelines, not guarantees. Quantization, conversation length, available VRAM, and other running applications affect what fits and how quickly it responds. Begin with a modest context length rather than immediately selecting the model's maximum.

Try the same request in two models and compare the answers. Ask each to rewrite an email or explain an Excel formula you already understand. That gives you a better feel for usefulness than asking a question when you cannot tell whether the answer is correct.

Understanding Model Sizes, Quantization, and Context

Model names can look like someone dropped a keyboard into a license plate factory. A few parts are worth understanding before you click Download.

The B in names such as 3B or 8B generally means billions of parameters. Parameters are numerical values learned during training. More parameters generally increase memory demands, but the number is not a universal intelligence score. A newer, smaller model can outperform an older, larger model on a particular task.

Quantization stores the model's numerical values at lower precision to reduce memory and storage requirements. You will see labels such as Q4 and Q8. A quantized model can be much easier to run on home hardware, with a possible tradeoff in answer quality.

Context is the material the model works with during a request, including instructions, conversation history, and supplied text. Longer conversations and larger documents generally require additional memory.

This is why a model's download size is not its complete running requirement. A 5GB model file does not mean 5GB of available memory will be enough. Your operating system, other applications, and the model's working data need room too.

Can Local AI Read Your Files or Search the Internet?

A downloaded model does not automatically know what is on your hard drive. Its application needs a feature that lets you supply documents or grant access to files.

It also does not automatically know today's news. A model needs current information supplied to it or an appropriate search tool. Being connected to the internet and actually checking a source are separate things, whether the model runs locally or in the cloud.

Local models can make things up, too... Just like the cloud versions. AI does not mean "accurate information". Keeping the computation on your desk does not make every answer correct. Check important claims, calculations, and instructions before relying on them.

What About OpenClaw and Agents?

Cloud versus local describes where a model runs. An AI agent describes how software uses a model and tools to carry out work.

A basic chatbot responds to your questions. An agent can go further, potentially reading files, running commands, operating a browser, or connecting to other services. The model helps decide what to do, while the agent software manages the tools and the steps involved.

OpenClaw belongs in this agent discussion. It can connect to different model providers, including local models through its Ollama integration.

Running an agent on your PC does not necessarily mean its model runs there. An agent can use a cloud model, a local model, or a combination, depending on its configuration. Where the agent runs, where the model runs, and where your information goes are separate questions.

The permissions matter as well. Asking AI to suggest how to organize a folder is different from allowing it to move and delete the files. Keeping the model local can help control where information is processed, but you still need to decide what the agent can access and change.

Agents also place additional demands on a model. A small model that handles conversation reasonably well may struggle to choose tools correctly or follow a task through several steps. Start with chat before handing it responsibility for your computer.

We will cover agents in a separate guide, including how they differ from chatbots and what to check before giving them access to your files and accounts.

Geek Verdict

Cloud AI is a practical choice when you want convenience, access to powerful models, and integrated tools without managing the hardware. Local AI is worth exploring when offline access, control over downloaded models, or keeping processing on your computer matters enough to accept some setup and hardware limits.

You can use both. A smaller local model might handle private writing and routine questions, while a cloud service handles tasks that benefit from its additional capabilities.

My advice is to start with the computer you already own. Try a small model, give it real work, and find out where it helps. Let an actual limitation justify the hardware upgrade.

Next, we will walk through installing a local AI application, choosing a model that fits your PC, and getting your first useful answer.

Jim McMahon Jim McMahon, aka Corporal Punishment, is the founder of MajorGeeks.com. He has spent decades testing software, troubleshooting Windows, and helping users cut through the nonsense. He loves real freeware, hates bloatware, and runs on caffeine, sarcasm, and questionable choices.

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