#AxlflopsKnowledgeSeries #8 ✅ What is a Tensor Core? 🖥️ Analogy: If a GPU is a factory, then Tensor Cores are like specialized machines built to quickly assemble the most complex parts of the production process—making AI computations much faster! ⚡️

#AxlflopsKnowledgeSeries #9 ✅ What is FLOPS, and how do you measure GPU performance? 🖥️ Analogy: If your GPU is a car, FLOPS would be its speedometer—showing how fast it can process data, with more FLOPS meaning faster and more efficient performance on heavy tasks! 🚗💨

#AxlflopsKnowledgeSeries #1 ✅ What is a GPU? A GPU (Graphics Processing Unit) is a specialized processor designed for high-speed computations. Originally built for rendering graphics, it is now widely used for AI and scientific computing. 🖥️ Analogy: Think of a GPU like a

#AxlflopsKnowledgeSeries #4 ✅ What is a CUDA Core? A CUDA Core is a processing unit inside an NVIDIA GPU that performs calculations in parallel, enabling faster computing for AI, graphics, and scientific applications. 🖥️ Analogy: If a GPU is a massive factory, then CUDA Cores

#AxlflopsKnowledgeSeries #2 ✅ GPU vs. CPU, which is more powerful? CPU excels at handling a few complex tasks, while GPU is designed to process thousands of smaller tasks simultaneously, making it ideal for AI and deep learning. 🖥️ Analogy: If a CPU is like a smart lawyer

#AxlflopsKnowledgeSeries #5 ✅ What is VRAM, and why does a GPU need it? VRAM (Video Random Access Memory) is the GPU’s dedicated memory, storing textures, models, and data needed for fast processing. More VRAM allows GPUs to handle larger datasets without slowing down. 🖥️

#AxlflopsKnowledgeSeries #3 ✅ Why is a GPU so fast?A GPU achieves high speed through parallel computing, allowing thousands of small cores to process data simultaneously, unlike a CPU that handles tasks sequentially. 🖥️ Analogy: Imagine a single artist painting a huge mural

#AxlflopsKnowledgeSeries #6 ✅ What is parallel computing? Parallel computing is a method where multiple calculations are performed simultaneously, dramatically speeding up processing compared to sequential execution. GPUs excel at this by using thousands of cores to work in

#AxlflopsKnowledgeSeries #7 ✅ What’s the connection between GPU computing and AI? GPU computing powers AI by efficiently handling massive amounts of data and performing complex calculations at high speeds, which is essential for training AI models and running deep learning