Model Parallelism
Advanced
Split model across GPUs. Understand how to train large models across multiple GPUs and machines.
Key challenges
- Communication overhead (AllReduce, synchronization)
- Load imbalance (some workers slower than others)
- Fault tolerance (any node can fail)
- Debugging (distributed race conditions)
Hints
Hint 1
Think about what needs to be communicated: gradients, model weights, optimizer state.
Hint 2
How do you measure efficiency: is it compute, communication, or synchronization?
Keywords
distributed
Python
Run your code to see results
No results yet
Run your code to check it against the examples, or submit to run every test.