GPUs are widely used in AI models and are worth billions of dollars. Firstly, GPUs were designed to display 3D images and videos in video games and computer software. Nowadays, GPUs also decompress video streams.
CPUs and GPUs have different architectures, with GPUs being more efficient for parallel computation tasks, while CPUs are better suited for sequential tasks.. CPUs are best for performing general tasks while GPUs are best for complex tasks.
A CPU is made up of about 8 to 6 cores but GPU is made up of thousands of tiny cores which work parallelly to achieve a task. This makes them good for tasks that need to perform a lot of operations at the same time. GPUs typically have 2 types; Standalone chips that work as an add-on card for large computers and GPUs combined with CPUs to make one chip package. These are found in game consoles like PlayStation 5. It's important to note that GPUs have their own control logic and can operate independently of the CPU, although they often work in tandem with the CPU for various tasks.
So how is the GPU used in AI? Most of the AI technologies work in forms of matrix multiplications. This technique uses mathematical operations to multiply large numbers and sum them up. These types of functions are similar to parallel working by GPUs and that’s why they can easily be performed by them. The number of cores in GPUs is increasing and as a result, its speed is also increasing. A company in Taiwan, TSMC, is working hard on improving the chip quality of GPUs. The size of individual transistors is decreasing and that’s why more transistors can be placed in that space. But even when GPUs are good for AI computation, they do not have optimal qualities to perform tasks.
GPUs are designed to accelerate computer tasks but there are also some accelerators that are designed to speed up machine learning tasks. Some of the companies that make these accelerators used to make traditional GPUs. Now, they have advanced in designs for machine learning and are called Data Center GPUs. Data Center GPUs and AI accelerators have more memory than traditional GPUs. So, Data Center GPUs are more suitable for large AI models. For handling large AI models like ChatGPT, multiple Data Center GPUs are combined to form a powerful computing system. This also requires complex software to handle all the power.
But CPUs are also performing a lot of functions. Many latest CPUs have power that speeds up the number crunching for required networks. To train the AI models, large GPUs are still needed and specialized accelerators should be created for machine learning algorithms. However, developing specialized AI accelerators requires significant engineering resources and can be prohibitively expensive for regular consumers.
Read next: Study Reveals Reading Aloud Enhances Memory, But Not Understanding
CPUs and GPUs have different architectures, with GPUs being more efficient for parallel computation tasks, while CPUs are better suited for sequential tasks.. CPUs are best for performing general tasks while GPUs are best for complex tasks.
A CPU is made up of about 8 to 6 cores but GPU is made up of thousands of tiny cores which work parallelly to achieve a task. This makes them good for tasks that need to perform a lot of operations at the same time. GPUs typically have 2 types; Standalone chips that work as an add-on card for large computers and GPUs combined with CPUs to make one chip package. These are found in game consoles like PlayStation 5. It's important to note that GPUs have their own control logic and can operate independently of the CPU, although they often work in tandem with the CPU for various tasks.
So how is the GPU used in AI? Most of the AI technologies work in forms of matrix multiplications. This technique uses mathematical operations to multiply large numbers and sum them up. These types of functions are similar to parallel working by GPUs and that’s why they can easily be performed by them. The number of cores in GPUs is increasing and as a result, its speed is also increasing. A company in Taiwan, TSMC, is working hard on improving the chip quality of GPUs. The size of individual transistors is decreasing and that’s why more transistors can be placed in that space. But even when GPUs are good for AI computation, they do not have optimal qualities to perform tasks.
GPUs are designed to accelerate computer tasks but there are also some accelerators that are designed to speed up machine learning tasks. Some of the companies that make these accelerators used to make traditional GPUs. Now, they have advanced in designs for machine learning and are called Data Center GPUs. Data Center GPUs and AI accelerators have more memory than traditional GPUs. So, Data Center GPUs are more suitable for large AI models. For handling large AI models like ChatGPT, multiple Data Center GPUs are combined to form a powerful computing system. This also requires complex software to handle all the power.
But CPUs are also performing a lot of functions. Many latest CPUs have power that speeds up the number crunching for required networks. To train the AI models, large GPUs are still needed and specialized accelerators should be created for machine learning algorithms. However, developing specialized AI accelerators requires significant engineering resources and can be prohibitively expensive for regular consumers.
Read next: Study Reveals Reading Aloud Enhances Memory, But Not Understanding
