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Local AI vs cloud AI: Why keeping your AI on your device can be the better option
In less than half a decade, artificial intelligence has become a core component of our everyday lives. We ask it tons of questions, whether work-related or personal. But have you ever thought about where those questions go?
Each query leaves your system, travels across the internet, and reaches the AI provider’s servers, where the processing happens, before the model returns an answer. Your words, including the private ones, end up on computers you do not control.
This is why local AI has been garnering a lot of attention over the past two years. Local AI essentially means that the AI brain sits inside your computer, and your data never leaves your system.
If you're confused about whether you should opt for local AI or cloud AI, let’s take a detailed dive in. Read on as we compare the two and find out which might be the better option for you.
What is local AI and cloud AI?
The distinction between cloud AI and local AI is pretty straightforward. The AI tools that an average user uses every day, such as ChatGPT, Gemini, or Claude, are examples of cloud AI. When you give your input to a chatbot, it travels across the internet to the AI provider’s server and returns with an answer. The whole transmission process requires an internet connection and access to the provider’s server.
However, local AI lives inside your computer or device and does all the processing locally. It doesn't need to access the provider’s server to give you answers. Once local AI has been downloaded and set up properly on a system, it can even work without an internet connection, with a few caveats.
Key advantages of using local AI
There is no one-size-fits-all approach when choosing between local AI and cloud AI. The choice ultimately depends on your needs and usage patterns. However, there are a few areas where local AI has an edge over cloud AI.
Privacy and control of your data
Privacy is perhaps the biggest argument in favour of local AI, since using cloud AI increases the surface area of private information you share with AI providers. A Stanford study of six leading AI companies – OpenAI, Meta, Google, Anthropic, Amazon, and Microsoft – revealed that all six of them use chat data to train their models by default.
Of course, some providers allow you to opt out of AI data training. However, you will have to consciously keep track of data usage by any LLM you use. Another issue is oversharing. As per a 2025 enterprise study, around 22% of employees who use generative AI feed it personal or payment card-related data.
The same report says that around 77% of people have been copying and pasting data into such LLMs, with 82% of pastes coming from unmanaged personal accounts, leaving enterprises with little to no visibility into what data is being shared. This massively increases the risk of sharing company secrets or confidential information with cloud-based LLMs.
The more data leaves your system and travels to and from the AI provider’s cloud infrastructure, the greater the potential exposure to data breaches or leaks. However, local AI reduces this risk. Since the data stays on your system, this becomes critical for sectors such as healthcare, finance, legal services, and businesses handling trade secrets.
Speed
With cloud AI, your question has to travel to a distant data centre and back before anything appears. With local AI, there’s no such journey, so the answer can start appearing sooner. As per a report, a round trip adds around 200 to 500 ms before the first word appears with cloud AI. On-device models, on the other hand, can produce each token in under 20 ms for shorter prompts.
This essentially means that local AI can start answering sooner, though it does not always finish sooner. For routine tasks, such as question-and-answer conversations, this hardly matters. But for tasks like translation or voice assistance, this delay can disrupt your experience.
Working offline
Cloud AI has one hard requirement: a working internet connection. Lose your signal on a flight or in a remote location, and your AI tool stops working altogether. Local AI has no such weakness, since the model runs locally on your device. It works just fine whether you are online or offline.
One important caveat to note is that you would still need an internet connection to download and set up the model on your device. But once that’s done, it’ll work even without an internet connection. This could come in handy for people who regularly experience patchy connections or live in remote locations where the internet connection is slow and unreliable.
Also, there’s always the chance of outages. ChatGPT, for example, experienced worldwide outages on July 25, 2026, and then again on August 19, 2026, each lasting about an hour.
A local AI model, however, works on your device, outside the reach of server outages, since nothing depends on an internet connection. That said, you won't be able to look up the latest news or stock prices using local AI without an internet connection.
Cost
You can think of cloud AI as taking a taxi and local AI as owning your car. With a taxi, you pay nothing upfront, but you pay each time you want to take a ride. With a car, you pay a lot at the start, but every subsequent trip costs you very little.
Cloud AI works the same way. The standard paid plans for cloud-based ChatGPT and Gemini cost about £20 a month, or £240 a year, for as long as you use them. Local AI has no monthly usage fees, but it comes with significant upfront costs, mainly attributed to hardware. So, which option is cheaper depends on how much you use it.
Researchers at Carnegie Mellon University compared the two approaches in 2025. They found that a $2,000 (£1,500) graphics card running a small AI model could pay for itself in under three months compared with paying a cloud company for the same AI job. This assumed that the machine was busy eight hours a day, 20 days a month. Larger models requiring more expensive equipment resulted in payback periods stretching to several years.
This cost has risen since the publication of the report, and the same graphics card would now cost somewhere between $3,500 (£2,625) and $4,000 (£3,000). As such, the payback period would stretch to around six months compared with before.
A 2026 Lenovo report also found the same pattern at a larger scale. A company running its own AI server around the clock could expect to spend around $1.5 million (£1.13 million) over five years, compared with $6.25 million (£4.7 million) for renting the same computing power from a cloud provider.
For most people, the takeaway is quite simple: if your phone or laptop is recent enough to run AI on its own, local AI would cost you almost nothing extra. If you need to buy new hardware just to run AI models, a cloud subscription might be a cheaper option if your usage is on the lighter side. However, for heavy users, local AI is still worth the expenditure.
Where cloud AI still wins
This doesn’t mean that local AI is automatically the best choice for everyone. There are areas where cloud AI is generally better. As per a report, cloud AI still excels at long chains of reasoning and complex problems that require deep thinking, particularly when compared with the smaller models that run on phones and lightweight laptops. For complicated maths problems or detailed plans where each step depends on the last one, cloud AI performs better.
Moreover, a small model struggles with broad world knowledge. A cloud model is like a full library, whereas a local model is closer to a single shelf. Larger models running on PCs with powerful graphics cards offer significantly more capabilities, although the biggest cloud-based models still have the edge.
An AI model also has to keep a running record of everything that has been said. As the conversation grows longer, local AI can struggle compared with cloud AI, particularly on devices with limited memory. Most of these issues come down to memory. Even expensive devices, such as the highest-end smartphones, can usually spare less than 4 GB of memory for AI, since other system resources and applications consume memory.
Plus, a phone moves data between its memory and processor 30 to 50 times more slowly than a data centre chip. This matters because the AI has to read through its whole model for every word it writes.
Desktop PCs and laptops with dedicated graphics cards are a different story. With more memory available for AI, they can run larger, more capable models, narrowing the gap with cloud AI for many tasks. Running AI models on phones and laptops can also cause devices to heat up and drain their batteries faster. Desktop PCs don't face the same battery limitations and generally have more room to manage heat.
To put it in a nutshell, what local AI can handle depends largely on your hardware. On a smartphone, it’s best suited for routine tasks such as summarisation and basic coding. On a PC with a capable graphics card, it can tackle more demanding work, including complex coding and analysing long documents. However, cloud AI still has the edge when it comes to the most challenging problems.
Local AI vs cloud AI: which one to choose?
Use local AI if...
- You have routine tasks to handle. Tasks such as rewriting, summarising, formatting, simple question-and-answer conversations, and basic coding can be handled well by local AI, even on phones and laptops.
- You have a powerful PC or laptop. If your device has a dedicated graphics card, local AI can handle more demanding tasks, such as complex coding, in-depth document analysis, and other advanced workloads.
- You deal with sensitive information. If you regularly work with health records, legal papers, financial accounts, or confidential company documents, local AI is a better choice for keeping your data private.
- You have a weak or patchy internet connection. Local AI can run without an internet connection, making it useful if you travel frequently or live in an area with unreliable connectivity.
- You need quick responses. If you rely on voice assistants or live translation, local AI can provide a smoother experience by reducing the delay associated with sending requests to remote servers.
Use cloud AI if...
- You need help with complicated problems. Cloud AI is generally better suited to complex reasoning, detailed plans, difficult maths problems, and tasks that require greater processing power, especially if your device can only run smaller models.
- You need fresh information from the internet. If you want to look up the latest news, stock prices, or other real-time information, cloud AI can be a better choice, particularly when it has web-search capabilities.
- You have an older device or limited memory. Cloud AI can be a better option if your hardware cannot run local models efficiently, since it removes the need for a costly upgrade.
- You use AI only occasionally. If you only need AI for a few tasks now and then, paying for a cloud AI subscription may be more economical than buying new hardware specifically to run local models.
Local first, cloud when needed
A hybrid model essentially means that the device handles whatever requests it can and passes only the harder requests to the cloud. If the task is too complicated for the local AI, the request goes to a larger model in a data centre. This is beneficial, since you still get the privacy, speed, and offline benefits of local AI most of the time, with the cloud’s power as a backup.
A Stanford-led study found that local models could accurately answer 88.7% of everyday chat and reasoning questions. Many companies, such as Apple, have already been adopting a hybrid model. When you first make a request to Apple’s intelligence models, the device checks whether it can handle it alone. Many requests never even leave your Apple device. If more power is needed, the requests are sent to Apple’s servers, a system called Private Cloud Compute.
Google also runs a small model on its phones called Gemini Nano and falls back on Google’s cloud model when the phone cannot handle the request. Researchers, in a study called “Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing,” built a router, which was a small program that read every question, predicted how difficult it was, and then sent it either to a small model or a larger one. The study found that up to 40% fewer questions were sent to the large model without any noticeable drop in the quality of answers.
All in all, your needs may not always be consistent. Sometimes, you might need greater processing power, for which cloud AI might be better suited, while most of the time, local AI would get the job done, especially for day-to-day tasks that require fewer resources. This is why it is important to adopt a hybrid approach so that you can get the best of both worlds.