Lilly Tech Systems
Learn TPU & AI Accelerators
Understand the specialized hardware powering modern AI. From Google TPUs and Apple Neural Engine to NVIDIA accelerators and performance benchmarks — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What are AI accelerators? Why specialized hardware exists and the landscape of AI chips.
2. Google TPUs
TPU architecture, systolic arrays, TPU pods, JAX/XLA integration, and Cloud TPU usage.
3. Apple Neural Engine
Apple Silicon NPU, Core ML, on-device inference, and the Apple ML ecosystem.
4. NVIDIA Hardware
A100, H100, Blackwell GPUs, Tensor Cores, NVLink, and NVIDIA AI platform.
5. Benchmarks
MLPerf, throughput vs latency, cost analysis, and choosing the right accelerator.
6. Best Practices
Hardware selection, cost optimization, framework compatibility, and future trends.
What You'll Learn
By the end of this course, you will be able to:
Understand AI Hardware
Grasp how TPUs, NPUs, and GPU accelerators differ in architecture and use cases.
Use Google TPUs
Train models on TPU pods using JAX and XLA, and optimize for systolic array execution.
Benchmark Performance
Evaluate accelerators using MLPerf, cost-per-token analysis, and workload-specific metrics.
Choose the Right Hardware
Select the optimal accelerator for your workload based on performance, cost, and availability.