Interactive diagrams teach you to see attention like a GPU, from GQA to Mixtral
vtabbott_ · x · 2026-09-28
An MIT author released an interactive website of attention mechanism diagrams designed to help readers "see like a GPU".
Highlights
- Frames attention in terms of tensor axes: batching, parallelization, streaming, and reductions all happen over axes — drawing them makes parallelization patterns (e.g., the q-axis) immediately visible.
- Covers scaled dot-product attention, causal self-attention with weights and residual connections, Multi-Head Attention, and Grouped-Query Attention (query heads sharing KV heads).
- Includes architecture diagrams of landmark models: Attention Is All You Need (2017), Mixtral-8x7B sparse MoE (2023), DeepSeek-V3 (latent attention + MoE, 2024), and more.
- Toggles between Forward/Decode/Cached passes, real-number vs quantized formats, and arrows/broadcast forms, with a companion notebook.
A solid resource for engineers and researchers wanting to understand Transformer internals and GPU parallelism.
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