Understanding Modern GPU Architecture for Machine Learning: H100 and B200 Technical Analysis
By
matt_d
9mo ago· 65 min readen
100/100
Golden Brown
Bagelometer↗
Hot, fresh, and worth queueing round the block for.
Score100Typehow-toSentimentneutral
Summary
This article provides a technical deep dive into modern GPU architecture, specifically focusing on NVIDIA GPUs like H100 and B200 used for machine learning. It explains that GPUs are essentially collections of compute cores specialized for matrix multiplication (Streaming Multiprocessors) connected to high-bandwidth memory. The content compares GPU architecture to Google's TPUs and explores how these chips work individually, how they're networked together, and the implications for large language model training and performance.
Key quotes
· 3 pulledA modern ML GPU (e.g. H100, B200) is basically a bunch of compute cores that specialize in matrix multiplication (called Streaming Multiprocessors or SMs) connected to a stick of fast memory (called HBM)
Each SM, like a TPU's Tensor Core, has...
This chapter takes a deep dive into the world of NVIDIA GPUs – how each chip works, how they're networked together, and what that means for LLMs, especially compared to TPUs