Local AI is AI that runs on hardware you own or control, such as your laptop, phone, or your organization's own servers, instead of on a provider's remote servers. In everyday terms, the AI model lives on your device and does its work there, without sending each request across the internet to a data center.
Here's how local AI works, why it's practical today, and where it fits compared to cloud AI.
How local AI works
Every time an AI model answers a question, it's performing inference, which is the process of using a trained AI model to produce an output from new input. The key difference between local AI and cloud AI is where inference happens.
With cloud AI, your request travels to a provider's data center, the model runs there, and the answer comes back. With local AI, the model is stored on your own hardware, and inference runs right there.
Think of it like the difference between streaming a song and playing one saved on your phone. The music is the same. What changes is where it plays from.
Running AI locally takes three things: a model downloaded to your device, software that runs it, and hardware with enough memory and compute to hold it. Most local AI runs on open-weight models, whose trained parameters are published so anyone can download and run them. Techniques like quantization, which stores a model's parameters at lower numerical precision, help those models fit on personal devices.
Local AI, on-device AI, and edge AI
These terms overlap. On-device AI is local AI that runs directly on the device you're using, and it's the most common kind. Organizations can also run local AI on their own servers.
Edge AI is AI that runs near to where data is created or used, instead of shipping that data to a far-away, centralized data center owned by a provider. Local AI usually counts as edge AI, but not all edge AI is local. Some providers use "edge" to describe their own distributed servers, which sit closer to users but are still someone else's hardware.
| Where inference runs | Who controls the hardware | |
|---|---|---|
Cloud AI | A provider's data center | The provider |
Local AI | Your device or your organization's servers | You or your organization |
On-device AI | The device you're using | You |
Edge AI | Near where data is created | Depends on the setup |
Why local AI is practical now
For a long time, running AI yourself meant settling for models well behind the best ones available from cloud providers. That gap has narrowed.
According to Stanford HAI's AI Index Report 2025, the leading closed-weight model outperformed the top open-weight model by 8.04% on the Chatbot Arena Leaderboard in early January 2024. By February 2025, that gap had narrowed to 1.70%. Chatbot Arena ranks models based on crowdsourced user preferences.
That matters for local AI because open-weight models are what most local setups run. Not every open-weight model fits on a laptop, but the models people can download and run themselves have moved much closer to the best closed models.
Intelligence is also getting smaller. According to the same Stanford report, the smallest model to score above 60% on the MMLU benchmark in 2022 had 540 billion parameters. By 2024, a model with 3.8 billion parameters cleared the same bar - small enough that a quantized copy typically fits and runs on a MacBook Pro with an M4 Pro and 16 GB of memory, not only on a data-center GPU. We explain why that shift matters in Why Smaller Models Win Where the Work Actually Happens.
What local AI is good for
Control over where your AI runs. Inference happens on hardware you control, so your prompts and the model's outputs are processed there instead of on a provider's servers. How the rest of an app handles data depends on the app. With webAI, you don’t have to hand private data to a third party that could use it to train or improve its own models.
Working without a connection. Once a model is on your device, inference doesn't depend on reaching a remote server. That helps at a remote job site or anywhere the internet is unreliable.
Predictable costs. Running your own model locally doesn't come with per-request fees, so you're using AI you own instead of renting it by the request. The upfront cost is hardware capable enough to run it. For why the real cost of renting AI goes beyond the per-request bill, see The Hidden Cost of Your Cloud AI Subscription.
Responsiveness. Without a round trip to a remote server, responses don't wait on network conditions. Actual speed depends on your hardware and the model.
The tradeoffs of local AI
Hardware matters. Memory and compute both limit what you can run. Memory decides whether a model’s weights fit on your device; compute decides how fast inference feels once it does. In other words, a model that runs smoothly on one machine may not run at all on another. Memory also affects a model's context window, meaning how much text it can consider at once, so very long documents or conversations can be harder to handle locally.
The largest models don't fit. The most capable frontier models run in data centers, and some demanding tasks still benefit from them. That said, the most capable model isn't automatically the right one for the job. We call that mistaken assumption the Frontier Fallacy.
Frequently asked questions
Is local AI private? Local AI keeps inference on your own hardware, so your prompts and outputs don't need to go to a provider's servers to be processed. Whether an app sends other data elsewhere depends on the app, so it's worth checking.
In the webAI app, prompts and outputs from local models stay on your device unless you choose a feature that sends them elsewhere, such as web search or connecting a cloud model. You can read the details in our app privacy policy.
Does local AI work offline? It can. Once a model is on your device, inference doesn't need an internet connection. "Local" describes where inference runs, though, so the software may still connect for updates or other features.
Is local AI free? Local inference doesn't carry per-request fees. You still need capable hardware, and some models and software come with their own costs.
Can I run local AI on my laptop? Often, yes, depending on your laptop's memory and compute and the size of the model. Smaller models run on a wide range of devices, while the largest models need far more capable hardware.
Is local AI less capable than cloud AI? It depends on the job. The very largest models run in the cloud, and for broad, open-ended tasks they often have the edge. But for specific work, bigger isn't automatically better. A smaller local model that's specialized for a task, grounded in the right context, and working alongside people and other models can be more effective than one general-purpose model trying to do everything. We make that case in Why Smaller Models Win Where the Work Actually Happens.
What is a local LLM? A local LLM is a language model running on your own hardware. Many local LLMs are small language models built to run efficiently on limited hardware.
The bottom line
Local AI comes down to where your AI runs. That choice shapes what you pay, what you control, and whether you need a connection to get work done.
At webAI, we're building tools for people and organizations to create teams of small, specialized models that run locally.