AI Academy

GPU environment deployment process

Complete Guide to Deploying GPU Servers1. System Initialization and Basic Configuration1.1 System Updates and Basic Tools 1.2 Creating a Deployment User (Recommended) 1.3 System Security Configuration2. NVIDIA Driver and...

In-depth analysis of the Nvidia H200 technology innovation redefines the standard of AI computing power

Introduction: The Dawn of a New Era in AI Computing PowerWith the explosive growth of large language models (LLMs) and generative AI applications, GPU memory capacity and bandwidth have become critical bottlenecks constr...

Chip-level Guardian: Technical Kernel and Arithmetic Assurance Logic for GPU Repair and Maintenance

In high-density computing scenarios such as large AI model training and simulation rendering, the stability of GPUs—as the "heart of computing power"—directly determines the success of business operations.A sin...

Multimodal AIGC Enables Film and TV Industrialization: Ape Arithmetic A100 Cluster Reduces Single-Frame Rendering Costs by 75

NVIDIA GPU H100 Architecture In-Depth Analysis

The NVIDIA H100 GPU is the ninth-generation data center-class GPU announced by NVIDIA at the GTC conference in March 2022. Based on the all-new Hopper architecture, it replaces the previous-generation Ampere architecture...

From "Arithmetic Silos" to "Optimal Solution Engines": Uncovering the Full-Stack Breakthrough of AI Clusters for Ultimate Performance

To ensure that an AI computing cluster delivers optimal results, systematic optimization is required across multiple dimensions, including hardware architecture, software optimization, task scheduling, and algorithm desi...

100kcal H100 Computing Cluster Build: From Technical Challenges to Implementation Options

IntroductionThe necessity of building a 100,000-card cluster is self-evident; currently, the threshold for joining the top tier of AI companies is already a 32,000-card cluster. It is projected that by 2025, this figure...

H20 Different Memory Versions: A Deep Analysis of Performance Differences and Large Model Adaptability

In the field of artificial intelligence, the NVIDIA H20 GPU has garnered significant attention for its design optimized for generative AI inference. The H20 is available in two memory configurations: 96GB and 141GB. Thes...

Hard and Soft: A Performance Panorama of Mainstream DeepSeek Deployments

When deploying the full-power version of the DeepSeek large language model, hardware selection is key to unlocking its full potential.From NVIDIA’s H200 and H100 series to domestic options such as Biren Technology’s BR10...

Which is better for deep learning, H200 or H100?

In the field of deep learning, the choice between the H200 and H100 should be based on specific task requirements, budget, and application scenarios. The following analysis examines these factors from the perspectives of...

What is the difference between NVIDIA H100 and A100?

Key Differences Between the NVIDIA H100 and A100The NVIDIA H100 and A100 are high-performance GPUs designed for different technology stages and application scenarios. Below are the key differences between the two: 1. **A...

"NVIDIA's GPU sparse arithmetic revolution: reinventing AI efficiency and leading a new era of cost reduction"

NVIDIA GPU "sparse computing" is an optimization technique designed to address the inefficiencies caused by the large number of zero-valued matrices in neural networks. The following is a detailed analysis of N...