Modern NVIDIA GPUs are feats of hierarchical design, optimized to maximize parallelism, minimize latency, and deliver staggering computational throughput. Building upon Part 1, which introduced the high-level architecture of NVIDIA GPUs, this is Part2 – a Deep dive into GPU Compute Hierarchy: Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), and CUDA…
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✅ Introduction to NVIDIA GPU Architecture: Hierarchy, Cores, and Parallelism
👋 Welcome to GPU Architecture 101 In this first post in five-part series, where I introduce you to the NVIDIA GPU architecture—a foundation for parallel computing used in gaming, scientific computing, artificial intelligence, and more. This guide is designed for engineering students and beginner developers who want to understand how modern GPUs work—from their evolution…
A Deep Dive into PyTorch’s GPU Memory Management
Here is an error I got when using an image generation deep learning model. It is a common error Engineers get when using PyTorch on GPU. To solve this error, a deep dive into PyTorch’s GPU Memory management is needed. So fasten your seat belts 🙂 torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 58.00…