GPU

CUDA is a parallel computing platform and programming model developed by Nvidia that allows developers to use GPUs for general purpose processing beyond graphics. It transformed Nvidia GPUs into essential tools for deep learning and high-performance computing by enabling efficient parallel processing of complex AI algorithms.

Nvidia GPUs feature thousands of efficient cores designed for parallel processing, high-bandwidth memory to minimize data transfer bottlenecks, and specialized tensor cores optimized for deep learning operations like matrix multiplication. These features enable faster computations, reduced training times, and efficient handling of large datasets typical in AI workloads.

The Lisuan G100 has shown significant improvement in recent benchmarks, scoring 111,290 points in OpenCL, which places it above the NVIDIA RTX 4060 and close to the RTX 5060. However, earlier tests indicated performance similar to the 13-year-old GTX 660 Ti, suggesting ongoing development and optimization.

Initial specifications listed for the Lisuan G100 included 32 Compute Units, 256 MB of VRAM, and a 300 MHz clock speed, which are likely placeholder values due to unoptimized firmware and drivers. The GPU is built on a 6nm process, and while it shows promise, it faces challenges in achieving stable performance and game optimizations.

FLOPS stands for Floating Point Operations Per Second, a unit measuring a computer's ability to perform floating-point calculations, which involve decimal numbers. It is important because it quantifies computational performance, especially for tasks requiring complex mathematical calculations such as scientific simulations, graphics rendering, and machine learning.

An exaflop is a measure of computing performance equal to one quintillion (10^18) floating-point operations per second. It represents an extremely high level of computational power, used to describe the capabilities of the most advanced supercomputers, enabling them to solve complex problems in science and technology.