DeepSeek's Complex MOE Implementation

DeepSeek's mixture of experts (MOE) model implementation is very complex, featuring a high sparsity factor (8 out of 256 experts activated).
This high sparsity requires splitting the model across different GPUs with various types of parallelism, leading to load balancing and communication scheduling challenges.
DeepSeek's innovation lies in changing the routing mechanism by adding an extra parameter to balance expert usage across batches, moving away from the auxiliary loss approach.
This approach, potentially a world first, addresses complexities like expert idling and resource optimization in sparse MOE models.
Their model requires complex parallelism to split the model and route traffic efficiently across different GPUs and avoid bottlenecks.

Transcript

Play full episode

Transcript

Episode notes

The AI-powered Podcast Player

Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!