h100
AI infra interview questions tagged h100, across every topic.
4 questions · 3 unlocked for you
Concepts behind "h100"
The curriculum that explains the ideas these questions test.
Foundational
Accelerator Selection: H100 to B300 and RTX PRO 6000Three published numbers decide which accelerator suits a workload, and they are independent: memory capacity gates what fits, memory bandwidth gates decode speed, and tensor FLOPS gate prefill and training. As of September 2026 the parts NVIDIA sells for datacenters span 80 GB to 288 GB and 1.6 TB/s to 8 TB/s, and the gap between the compute number and the bandwidth number has widened every generation, which is why a part that looks four times faster on a slide is often twice as fast on a decode workload.🖧 Hardware & Cluster Build-Out
Advanced
GPU Generations: A100 to BlackwellFour NVIDIA generations are in fleets at once, and interviewers ask what each one changed, not what it is called. A100 to H100 added fp8 and tripled compute; H200 kept the die and grew memory; B200 doubled everything and added fp4; B300 stacked more HBM and cut fp64. This page carries the dense numbers for each, what they did to training and serving, and the marketing traps (sparse peaks, 192 versus 180 GB, die counting) that trip candidates. Dated September 2026.🧩 GPU & Accelerator Architecture🔒 Premium
