Gpu

Introduction Large Language Models (LLMs) such as GPT‑4, LLaMA, and BLOOM have transformed natural‑language processing, but training and serving them at scale demands massive GPU resources, high‑speed networking, and sophisticated orchestration. Amazon Elastic Kubernetes Service (EKS) provides a managed, production‑grade Kubernetes platform that can run distributed GPU workloads, while integrating tightly with AWS services for security, observability, and cost management. This article walks you through end‑to‑end optimization of distributed GPU workloads for LLMs on Amazon EKS. We’ll cover: ...

Introduction Training modern deep‑learning models often requires hundreds of gigabytes of data and billions of parameters. A single GPU can no longer finish the job in a reasonable time, so practitioners turn to distributed training. While data‑parallel synchronous training has become the de‑facto standard, asynchronous stochastic gradient descent (ASGD) offers compelling advantages in elasticity, fault tolerance, and hardware utilization—especially in heterogeneous or spot‑instance environments. At the same time, Kubernetes has emerged as the leading platform for orchestrating containerized workloads at scale. Its declarative API, built‑in service discovery, and robust auto‑scaling capabilities make it an ideal substrate for running large‑scale ML clusters. ...

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: ...