NVIDIA H100 Specs Explained — SXM vs PCIe, VRAM & H100 vs A100

The NVIDIA H100 spec sheet is full of numbers — but which ones actually matter for your workload? Mike San Miguel from Luxor's AI sales team breaks down the H100 specs that move the needle and the ones that are just marketing. In this H100 specs deep dive: the Hopper GH100 die, SXM vs PCIe (they are NOT the same product), VRAM and memory bandwidth, the Transformer Engine and FP8, NVLink, MIG partitioning, and a quick H100 vs A100 comparison — all framed around what each number means for real AI training and inference deployments. ⏱️ Chapters 0:00 Which H100 specs actually matter 0:20 The die: Hopper GH100, CUDA & Tensor cores, L2 cache 1:23 SXM vs PCIe — two different products 3:05 The Transformer Engine (FP8 ⇄ FP16) 4:14 Memory: why 80GB is the number that matters 5:04 MIG: split one H100 into 7 instances 5:57 H100 vs A100 6:45 Recap + talk to Luxor 📺 Full NVIDIA H100 series ▶ Overview:    • NVIDIA H100 Overview | What It Is & Where ...   ▶ Specs Deep Dive: this video ▶ Best Workloads & Use Cases: coming soon ▶ Compatible Server Systems: coming soon ▶ Economics & TCO: coming soon 📰 Companion read on Hashrate Index — Inference vs Training: https://hashrateindex.com/blog/ai-inf... 📧 Comparing GPUs for a real deployment? Talk to Luxor's hardware desk: [email protected] 🔔 Subscribe for GPU market updates and hardware deep dives. #NVIDIAH100 #GPU #AIInfrastructure