> Canonical page: https://posttrainllm.com/learn/paths/architecture-and-kernels

Learning path

# Architecture and kernel execution

Match one optimized attention or matmul path against the readable reference and identify the real bottleneck with a measured curve. Mastery is demonstrated when the learner can explain the numerical contract, memory traffic, and why a microbenchmark may not improve end-to-end training.

**Modules**
 5

**Prerequisites**
 2

**Recipes**
 2

**Buildable artifacts**
 1

## Prerequisites

- foundations
- training mechanics

## Learning sequence

- Reference transformer math
- Attention memory traffic
- Precision and tiling
- Sparse versus dense MoE
- Correctness before performance

## Hands-on lab

Match one optimized attention or matmul path against the readable reference and identify the real bottleneck with a measured curve.

## Mastery gate

Explain the numerical contract, memory traffic, and why a microbenchmark may not improve end-to-end training.

## CLI surface

- Run: posttrainllm bench
- Run: posttrainllm bench-train
- Run: posttrainllm experimental train-heads

## How to read this dossier

This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.

Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.

To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.

## Source provenance

The normalized record comes from docs/learn/path-registry.json . The links below are the tracked evidence and explanatory sources preserved with the record.

- [docs/learn/llm-mechanics-fundamentals.md ↗](https://posttrainllm.com/docs/learn/llm-mechanics-fundamentals/)
- [docs/learn/essential-vs-optimization.md ↗](https://posttrainllm.com/docs/learn/essential-vs-optimization/)
- [docs/performance/fa2_forward_notes.md ↗](https://posttrainllm.com/docs/performance/fa2_forward_notes/)
- [docs/performance/fa2_backward_notes.md ↗](https://posttrainllm.com/docs/performance/fa2_backward_notes/)
- [docs/techniques/moe.md ↗](https://posttrainllm.com/docs/techniques/moe/)
- [docs/learn/webgpu-execution-model.md ↗](https://posttrainllm.com/docs/learn/webgpu-execution-model/)
- [docs/industry_learning_roadmap.md ↗](https://posttrainllm.com/docs/industry_learning_roadmap/)

[Read the Markdown equivalent →](https://posttrainllm.com/learn/paths/architecture-and-kernels.md)

Related evidence

- recipe [Sparse mixture of experts](https://posttrainllm.com/recipes/moe)
- recipe [Precision and numerical gates](https://posttrainllm.com/recipes/precision)
- artifact [Kernel parity and speed receipt](https://posttrainllm.com/learn/artifacts/kernel-parity-receipt)

 Canonical record learning-path:architecture-and-kernels