What is a GPU?

Originally designed to render graphics in video games, GPUs have changed the game in AI with their unrivaled parallel processing capabilities, such as deep learning and image processing.

Unlike CPUs, GPUs excel at performing thousands of computational tasks simultaneously in parallel, making them essential for training and operating complex neural networks.

• Parallel processing power - With thousands of cores optimized for parallel computing, GPUs enable increasingly realistic graphics. They also dramatically accelerate AI workloads, reducing training times from weeks to hours.
• Scalability - By harnessing the power of multiple GPUs in parallel, organizations can seamlessly scale their AI infrastructure to meet their evolving needs.

• Specific use cases - While GPUs excel at parallel processing tasks, they may not be as efficient at sequential or single-threaded applications, limiting their versatility

GPU stands for Graphics Processing Unit. It's a specialized processor designed to accelerate graphics rendering and perform complex calculations required for visual tasks. GPUs are commonly used in gaming, video editing, and applications involving 3D graphics and machine learning.

Several companies are well-known for manufacturing GPUs (Graphics Processing Units). Here are some of the key players in the industry:

1. NVIDIA: A leading company in GPU design, known for its GeForce series for gaming and Quadro series for professional graphics.
2. AMD (Advanced Micro Devices): Produces Radeon GPUs, which are popular in both gaming and professional markets.
3. Intel: Recently entered the discrete GPU market with its Intel Arc series.
4. Apple: Designs its own GPUs for use in its devices, such as the M1 and M2 chips.
5. Qualcomm: Known for its Adreno GPUs used in Snapdragon processors for mobile devices.
6. Samsung: Manufactures Exynos processors, which include integrated GPUs for its smartphones.
7. ARM: Designs GPU architectures used by various manufacturers in mobile and embedded systems.