Edge AI vs Cloud AI: Key Differences and Benefits
Desktops
AI technology development has become rapid in recent years. However, most AI computing must run on the internet due to hardware and software limitations, which is called Cloud AI. It will cause a network latency problem, but it is still good for users because AI functions are very useful in many professional fields. For example, information summarization, report analysis, or data comparison.
Last year, there was a large language model release that made AI computing like a rocket. So, now people can easily run the AI program on a local PC system without an internet connection and solve the network latency issue. Which is called Edge AI. Today, we will run down to you on how Edge AI and Cloud AI differ and the benefits for people.
AI function benefits our daily workflow, and more and more functions are being developed to change people's future lives.
Before the AI chatbot release, if people had questions, we would look for the answer on Google. Or search for the information in the library. Even looking for a professional consultant. But since ChatGPT was released, it has changed people’s behavior. They start to ask questions to ChatGPT and get the answer quickly and precisely. And more AI chatbot services are showing up, so people have more choices to use the chatbot. However, they are all Cloud AI services and need to run on an internet connection. Cloud AI may encounter some problems when we use it in a professional field.
Cloud AI could make your data insecure, but Edge AI does not.
Normally, an enterprise or a company always has a data security issue. It is dangerous to leak customers’ information or internal confidential information when we use any AI services. The Cloud AI could have this situation when we upload data to ask AI computing, summarize, analyze, and produce a report. We don't know whether the data we upload could be used by 3rd third-party vendor or a hacker to hijack the data to use. That’s why we need Edge AI to solve this problem.
Network latency and limited by the AI services credit.
Those chatbot AI services are based on the large language models to build. This is why most AI services use a cloud GPU server for computing. It needs a huge storage space to save and VRAM to run. When many people inquire from the AI services, it could result in a latency or delay issue. Because all people use the same GPU server for the inquiry, more people could more slower performance. Moreover, some AI services need to pay for a subscription or have usage limitations by credit. It will be a cost for the company or enterprise.
Edge AI breaks through the limitations and offers a secure and flexible environment to use AI functions
Because language models’ distillation techniques have improved a lot. The LLM (Large Language Model) can be shrunk to a small size and run with lower hardware requirements. Can also run a chatbot on a local PC system without an internet connection. And more LLMs are being published this year, which creates a positive competition for AI models, also good for the users. People can not only use one LLM in a system, but also can use other LLMs which more flexible. The most important thing Edge AI can solve is the network latency and data security issues. People don’t need to worry about the risk of data leaks. This is the benefit of using Edge AI.
The AI model will become more and more popular everywhere, users can easily apply any kind of model in their system by the Edge AI concept.
For more information about the MSI AI PC solution, please check here: https://www.msi.com/Landing/MSI-AI-PC-Desktops
