Inference Engines charts token flow through transformer layers

This is the rare transformer explainer that is actually worth the scroll because it turns every stage into a mental model you can use when debugging throughput.

• –It ties each transformer stage to actual compute, which is exactly what builders need to reason about latency and bottlenecks.
• –GPT-2 vs Qwen examples make the scale penalty tangible: same pipeline, far more matmuls and FLOPs at larger sizes.
• –The visual walkthrough plus code bridge theory to implementation, so it should land with both beginners and engineers who already tweak inference stacks.
• –Because this is Part I, the obvious follow-up is KV cache, batching, quantization, and kernel-level optimization work.