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Distributed training basics

**Distributed training** splits work across accelerators or machines so larger models and batches finish in useful time. In MLOps terms it is one step inside a reproducible training pipeline—not a one-off laptop run. <!-- IMAGE: one GPU ...

What it is

Distributed training splits work across accelerators or machines so larger models and batches finish in useful time. In MLOps terms it is one step inside a reproducible training pipeline—not a one-off laptop run.

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Why it matters

Frontier and even mid-size fine-tunes are systems problems: networking, stragglers, checkpoint recovery, and correct gradient aggregation. Stanford CS329S treats scaling and distributed training as part of *systems design*, not only algorithm choice.

How it works (plain)

Common pattern (data parallelism): copy the model to many devices, split the batch, compute gradients, average them, step together. When the model does not fit on one device, use tensor or pipeline parallelism to split layers/weights. Always checkpoint so a failed worker does not erase days of compute.

Everyday example

Many cooks chopping vegetables in parallel, then combining into one pot—coordination overhead is real. One slow cook (straggler) can stall the whole meal.

Try it

List what breaks if one worker is slow or drops offline mid-run. Write the recovery step you would want automated (reload last checkpoint, exclude bad node, resume).

Myths

⚠️ Myth: 8 GPUs always train 8× faster.
✓ Reality: Communication overhead eats gains; small models may slow down.
⚠️ Myth: Distributed is only for Big Tech.
✓ Reality: Small teams hit multi-GPU fine-tunes and shared cloud jobs.
⚠️ Myth: “It finished” means “it trained correctly.”
✓ Reality: Silent sync bugs and version skew across workers create wrong models that still emit metrics.

Sources