The article explains how the Transformer architecture replaces RNNs with self‑attention, detailing the Q‑K‑V mechanism, positional encodings such as RoPE, multi‑head attention, modern improvements like SwiGLU and RMSNorm, and provides formulas for parameter and FLOP estimation.
Preparing for large-model interview? This guide reveals why interviewers probe seemingly minor components—positional encoding, residual connections, layer normalization, and feed-forward networks—explains each technique's purpose, variants, and how to answer confidently, plus practical tips and a learning roadmap to boost your chances.
This article provides a comprehensive, engineer‑focused breakdown of transformer architecture—including tensor fundamentals, matrix multiplication, GPU theoretical compute, attention and FFN mechanics, quantitative parameter and FLOP analysis, performance metrics like MFU, parallelism strategies, variant optimizations, and practical exercise questions—offering clear insight into large‑model efficiency and scaling.
