AI Hardware

Introduction In the world of large language models (LLMs), memory is a critical bottleneck. RAM (system memory) and VRAM (video RAM on GPUs) serve distinct yet interconnected roles in training and running models like GPT or Llama. While RAM handles general computing tasks, VRAM is optimized for the massive parallel computations required by LLMs.[1][3][4] This detailed guide breaks down their differences, impacts on LLM workflows, and optimization strategies, drawing from hardware fundamentals and real-world AI applications. ...

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] ...

As an expert AI infrastructure and hardware engineer, this tutorial takes developers and AI practitioners from zero knowledge to hero-level proficiency with NVIDIA hardware for large language models (LLMs). NVIDIA GPUs dominate LLM workloads due to their unmatched parallel processing, high memory bandwidth, and specialized features like Tensor Cores, making them essential for efficient training and serving of models like GPT or Llama.[1][2] Why NVIDIA GPUs Are Critical for LLMs NVIDIA hardware excels in LLM tasks because of its architecture optimized for massive matrix multiplications and transformer operations central to LLMs. A100 (Ampere architecture) and H100 (Hopper architecture) provide Tensor Cores for accelerated mixed-precision computing, while systems like DGX integrate multiple GPUs with NVLink and NVSwitch for seamless scaling. ...