This article explains graphics card fundamentals, covering GPU architecture differences from CPUs, key components like VRAM and cooling, integrated vs discrete GPUs, the five-step rendering pipeline, frame rate targets, resolution impact, ray tracing technology, and non-gaming uses such as video editing and AI training.
This article breaks down how GPU compute capability, VRAM capacity, memory bandwidth, and inference frameworks each determine whether a graphics card can run large language models and how fast, using a factory analogy to show why raw specs alone are misleading.
This guide explains how to evaluate quantized large language models by measuring VRAM reduction, inference performance (latency, throughput), and model quality (perplexity, accuracy, BLEU/ROUGE), providing step‑by‑step procedures, code examples, and best‑practice recommendations for deployment.
The article explains the layered memory hierarchy of a computer—from ultra‑fast registers and multi‑level CPU caches to RAM, SSD/HDD storage, and dedicated VRAM—using everyday analogies and concrete examples, and shows why “out of memory” warnings appear.
This article introduces three open‑source tools that improve GPU‑centric development: NBD VRAM, which turns GPU memory into Linux swap space; Reg Factory, a scheduler and monitor for multi‑GPU clusters; and vLLM Studio, a web UI for deploying and managing large‑model inference.
