Getting Technical with Mashable Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Gift Ideas For Everyone On Your List Say More Versus Trending Now In My Bag Mashable @Home Top creators, ranked Creator Playbook All Series If you're ready to start using AI on your PC, here are some things you'll want to know first. AI is here to stay, it seems, and a lot of people have leaned into the change rather than resisting it. Many people engage with AI over their web browser since major AI services are available there, thereby bypassing the need to have a strong PC that's optimized to run AI. However, other folks want to run AI directly on their PCs. That is a different category entirely.

When people talk about the "size" of an AI model, they're referring to the number of "parameters" it contains. A parameter is one variable in the AI model that determines how it generates output, and any given AI model can have billions of these parameters. Also referred to as model weights, these parameters occupy storage space to operate properly -- and when an AI model has billions of parameters, storage requirements can quickly balloon. As you can see, the storage space consumed by an LLM increases with the size of its parameters. The same is true for other types of generative AI models, too.

Artificial intelligence algorithms cannot keep growing at their current pace. Algorithms like deep neural networks -- which are loosely inspired by the brain, with multiple layers of artificial neurons linked to each other via numerical values called weights -- get bigger every year. But these days, hardware improvements are no longer keeping pace with the enormous amount of memory and processing capacity required to run these massive algorithms. Soon, the size of AI algorithms may hit a wall. And even if we could keep scaling up hardware to meet the demands of AI, there's another problem: running them on traditional computers wastes an enormous amount of energy.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! There is no shortage of options for organizations seeking places in the cloud, or on-premises to deploy and run machine learning and artificial intelligence (AI) workloads. A key challenge for many though is figuring out how to orchestrate those workloads across multi-cloud and hybrid-cloud environments. Today, AI compute orchestration vendor Run AI is announcing an update to its Atlas Platform that is designed to make it easier for data scientists to deploy, run and manage machine learning workloads across different deployment targets including cloud providers and on-premises environments.