user
You mention expertise in 'LLM Local & Cloud Deployment'. Could you outline the key differences and considerations when deploying Large Language Models locally versus in the cloud?
assistant
This goes back to the real world example, as it always does!It's important to know that the LLMs are going to be different in the cloud vs locally. Also, the way that you architect them, is going to be quite different too. It also means that if you are deploying to the cloud, you can scale the number of instances.There are different considerations for different architectures. For example, if you are deploying on Azure, you need to know the specific deployment steps as you would for deploying to AWS or other systems.For running it locally, you need the correct hardware and software configurations. For running it locally, you need to use all the resources to their maximum potential. Whereas, the Cloud is on-demand. So, it really does make a huge difference.There are big security and compliance concerns as well because of all of the data. There is definitely a good case to deploy the LLMs locally, and not put everything on the cloud. Key differences between local and cloud LLM deployment involve hardware and scaling considerations. Local deployment requires robust local hardware and specific software configurations, while cloud deployment offers on-demand scalability and diverse architectural options. Security and compliance are also critical considerations, often favoring local deployment for sensitive data.