GPU

In the world of computing, CPUs, GPUs, and TPUs represent distinct architectures tailored to different workloads, with CPUs excelling in general-purpose tasks, GPUs dominating parallel processing like graphics and deep learning, and TPUs optimizing tensor operations for machine learning efficiency.[1][3][6] This detailed guide breaks down their architecture, performance, use cases, and trade-offs to help you choose the right hardware for your needs. What is a CPU? (Central Processing Unit) The CPU serves as the “brain” of any computer system, handling sequential tasks, orchestration, and general-purpose computing.[3][4][5] Designed for versatility, CPUs feature a few powerful cores optimized for low-latency serial processing, making them ideal for logic-heavy operations, data preprocessing, and multitasking like web browsing or office applications.[1][2] ...

Introduction If you’re trying to serve large language models (LLMs) efficiently on GPUs, you quickly run into a wall: GPU memory gets eaten by KV cache Throughput collapses as concurrent users increase You spend more on hardware than on your actual application vLLM is an open-source inference engine designed to fix this. It combines: A highly optimized attention implementation (PagedAttention) Continuous batching and scheduling A production-ready API server (OpenAI-compatible) Tight GPU memory management This tutorial is a concise zero-to-hero guide for developers who want to: ...