seniorGradient Descent
What is gradient descent in distributed systems?
Updated May 16, 2026
Short answer
Distributed Gradient Descent splits computation across multiple machines.
Deep explanation
In distributed training, gradients are computed on multiple nodes and aggregated using parameter servers or all-reduce operations. This enables scaling to large datasets and models.
Real-world example
Training large language models across GPU clusters.
Common mistakes
- Ignoring synchronization overhead.
Follow-up questions
- What is synchronous vs asynchronous GD?
- What is parameter server?