This article analyzes NVIDIA's A100, H100, A800, H800, and H20 GPUs, compares their architectures, performance, and pricing, and provides a step‑by‑step guide for building a private AI compute center tailored to training, inference, and high‑performance computing workloads.
This article provides a detailed technical breakdown of modern multi‑GPU server nodes, covering component composition, storage network cards, NVSwitch interconnects, bandwidth calculations, and the architectural differences between NVIDIA A100/A800 and H100/H800 configurations for AI training workloads.
This article compares NVIDIA's high‑end AI GPUs—H100, A100, and the newer L40S—detailing their specifications, performance trade‑offs, pricing, availability, and suitability for training and inference workloads, while highlighting why L40S can be a cost‑effective alternative for many enterprises.
The article provides a detailed technical analysis of NVIDIA's Hopper‑based H100 GPU, covering its 4 nm process, 800 billion transistors, GPC/TPC hierarchy, new FP8 Tensor Cores, Transformer engine, Tensor Memory Accelerator, and the resulting six‑fold performance jump over the previous A100 generation.
At the recent GTC event, NVIDIA introduced the H100 GPU built on the Hopper architecture using TSMC 4nm process, featuring 800 billion transistors, 16,896 CUDA cores, up to 700 W power, 3 TB/s memory bandwidth, and a specialized Transformer engine that accelerates large‑model training up to six times faster, alongside the Grace CPU Superchip and new AI supercomputing systems.
