# AI Hardware & Validation

> End-to-end validation for AI accelerators — hardware bring-up, kernel correctness, model performance, and datacenter-scale testing.

![Status: complete](https://img.shields.io/badge/status-complete-brightgreen)

Open [`29-ai-hardware-llm-validation.ipynb`](./29-ai-hardware-llm-validation.ipynb) for learning objectives, prerequisites, and recommended reading order.

## Topics Covered

- Hardware validation: power, thermals, memory, and stability
- Kernel correctness: GEMM, conv, attention, softmax, and layernorm
- Model performance benchmarking: throughput, TTFT, and latency
- Distributed training validation and datacenter observability
- Industry benchmarks: MLPerf, AA-SLT, and LMSys Arena

## Prerequisites

[06-neural-networks/README.md](../06-neural-networks/README.md) · [09-mlops/README.md](../09-mlops/README.md) · Linux proficiency.

## What Comes Next

[30-inference-optimization/README.md](../30-inference-optimization/README.md)
