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AI Hardware & Compute

The silicon and systems that make modern AI possible — and the single biggest practical constraint on what gets built.

AI Hardware & Compute is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart TB
  MODEL[Model + data] --> TRAIN{{Training cluster<br/>GPUs / TPUs}}
  TRAIN --> CKPT[(Checkpoint)] --> OPT[Quantize / compile]
  OPT --> SERVE[[Inference server]] --> APP[/Application/]

Key topics

  • GPUs, TPUs & accelerators

    Why massively parallel hardware dominates deep learning, and the chips that run it.

  • The memory wall

    HBM, bandwidth, and why memory — not raw FLOPs — is often the real bottleneck.

  • CUDA & kernels

    The software stack that maps math onto hardware; fused kernels like FlashAttention.

  • Quantization & precision

    FP16, BF16, INT8 and 4-bit — trading numerical precision for speed and memory.

  • Inference optimization

    Batching, KV-caching, speculative decoding, and serving models efficiently.

  • Scaling laws & cost

    How compute, data, and model size trade off — and what a training run actually costs.

Deep Learning · Data & MLOps · Edge & On-Device AI

Learn this properly

Want hands-on training in ai hardware & compute? Explore AI University courses and AI School camps for kids.