Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Artificial Intelligence software is more popular than ever, and cloud-based AI systems feature the most powerful models. OpenAI’s GPT-5, X’s Grok 4, Anthropic’s Claude 4, Meta’s Llama 3, and Google’s Gemini 2.5 are all massive large language models used for a variety of purposes, including text and image generation, document summaries, and conversation partners.
However, despite the advantages of massive cloud-based language models, I’d rather use an on-device AI agent instead. Here are four reasons why you should consider switching from that cloud-based AI subscription to a local, on-device AI instead.
One of the major drawbacks to cloud-based generative Artificial Intelligence is the steep environmental cost of running massive AI data centers. Not only do these data centers require enormous amounts of power, but they also require a lot of water cooling to keep the GPUs from melting. While more data centers are transitioning to saltwater cooling, this shift also has an impact on ocean ecosystems.
Running your AI locally may see a bit of an increase to your monthly power bill, but hosting an AI on your home PC requires significantly less power than a data center. And even if you use a water cooling system for your rig, it will use far less water to cool your home PC than a massive data center. While switching to on-device AI won’t stop massive data centers from operating, it can reduce your personal energy footprint, which is a welcome bonus.
If you want to send personal documents to the cloud for any kind of AI summarization or searching, you need to do a lot of masking to prevent accidentally sending sensitive information to be indexed by a cloud-based AI. Your personal information could be leaked to other users, the company running the AI, or even be hacked if the data center faces a security breach.
Some cloud-based AI systems have rules in place to mask your personal data before sending it to the cloud, like Apple Intelligence, but many don’t. The AI may also advise against sending your personal photos or data, but this’s highly dependent on the safety guardrails of the chatbot in question. So it’s not a rule across the board. Instead, your personal security when it comes to AI is mostly left in your own hands, for better or worse.
One easy way to avoid the potential security concerns of cloud AI is to build your own AI entirely on your personal device using a tool like Intel’s AI Assistant Builder, or use one of the local AI chatbots like Local AI, Pocket Pal, or Private AI.
However, while Microsoft’s Copilot+ Recall system is also stored on your device, we would still caution against using it because it creates a searchable database of your PC, including sensitive data. Therefore, it can pose a significant security liability if your own device is targeted.
Because localized AI is more secure, you can submit your personal data without worrying in the same way you would with a cloud system. There are always some caveats here, because you don’t want to create a system that hands hackers all of your personal identifying information. But you can train a local AI so it learns your needs and is customized to your own purpose. This is true of local chatbots as well as local language models like the Intel AI Assistant.
While cloud-based AI is generally considered smarter than the smaller language models you can use on a device, those AI systems can’t be fully customized. They often have limited memory retention and will revert to the default state after some time. While this is fine for general queries, depending on what you plan to use your AI for, it’s not a great long-term solution.
To really get the most out of a cloud-based AI system, you often need to subscribe. While paying $20 a month to ask ChatGPT more than 5 questions isn’t a lot, it does add up over the course of a year. There are free cloud-based chat systems, including the free tier of ChatGPT and Google Gemini, but the free tier only gets you so far.
Not all on-device AI is free, but a lot of it is. Local AI, LMStudio, GPT4All, and AI Assistant Builder are all local, free AI tools that don’t have a higher, paid subscription tier. Because they’re open-source AI tools, you may need to do a little bit of work to get them up and running to similar levels of performance as you’d expect of ChatGPT or Gemini, but you get access to the full system for free. Which you can’t say about cloud-based AI systems.
Even though on-device AI is more secure than its Cloud counterparts, you should always maintain a level of caution any time you’re dealing with any computer program. It’s like making sure you don’t use the same password for every login, and make sure you don’t submit all of your personal data to an AI chatbot that can be easily searched in case of a security breach.
And while local AI customization is a huge benefit, AI isn’t perfect, so you’ll still need to do your own fact-checking when asking it questions. You’ll also want to be careful that the AI doesn’t become an echo-chamber, which is all too easy to do with the way language models are currently coded.
However, if you plan to utilize AI assistants, opting for secure, free on-device AI is the best approach.
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I use AI for vibe coding. I would LOVE if i could host a great model on my own system, but I’m worried about the quality.

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