In today’s dynamic world, graphic processing units(GPUs) are the core of AI processing. They also play a significant role, especially in data centers. Typically, GPUs offer unmatched cutting-edge performance for AI inference and training. As a result, they are drawing organizations’ appetites while influencing them to invest in computing and storage capacities. Graphic processing units are usually divided into two, including integrated and dedicated GPUs. This article will explore everything you need to know about GPUs, including why you need a dedicated GPU for AI development.
But first, let’s see how GPUs differ from CPUs.
What is the Difference Between CPUs and GPUs?
GPUs and CPUs have numerous standard features. For instance, both handle data, are silicon-based microprocessors and involve critical computing engines. However, it is essential to note that they have varying architectures and are built for different reasons.
To begin with, CPUs are ideal for tasks, especially those with crucial per-core performance or latency. A CPU is a powerful execution engine that focuses its reduced cores on single tasks and gets things done fast. Therefore, CPUs are uniquely sufficiently equipped for tasks such as running databases and serial computing, where a single processor executes one task at a time.
GPUs, on the other hand, started as application-specific integrated circuits (ASICs), established for specific purposes like accelerating 3D rendering tasks and graphics such as gaming. These fixed-function engines developed over time, resulting in more flexible and programmable engines. Even though hyper-realistic gaming visuals and graphics are their core function, graphic processing units have evolved to become more general-purpose parallel processors. They are able to execute various tasks concurrently, thus handling an increased range of applications like AI.
Integrated GPUs Vs. Dedicated GPUs
Integrated GPUs
Also referred to as integrated graphic cards or integrated graphics, integrated GPUs are established directly from the computer’s processor. This means that they share power between the CPU and GPU. Integrated GPUs offer various benefits, including cost-effectiveness, space savings, and energy efficiency. Additionally, they are sufficient for everyday tasks since they can handle primary computing requirements nicely.
When Should You Choose Integrated Graphics?
Integrated graphics are perfect for users who engage in the following tasks:
● Video content streaming
● Basic gaming with fewer demanding titles
● Casual Office productivity like spreadsheets and word processing
● Social media and web browsing
● Light photo editing
Dedicated GPUs
Also known as dedicated graphic cards or discrete GPUs, dedicated GPUs are built separately from the CPU. This means that they have individual dedicated memory, unlike integrated GPUs, which share. The good thing about these graphics is that they provide exceptional performance, especially for graphics-intensive operations.
Dedicated GPUs offer advantages, including dedicated memory, advanced performance, superior features, and the ability to be upgraded.
When Do You Need Dedicated GPU?
● A dedicated GPU is essential for the following tasks:
● Multi-monitor setups
● Content creation and video editing
● 3D rendering and modeling
● Advanced Gaming
● Engineering and CAD applications
● AI development and machine learning
Dedicated GPUs are critical for AI development since they offer advanced features, dedicated memory, and impeccable performance. Therefore, your AI developments can remain functional with maximum efficiency. Check here for the best GPUs for AI development.