EPLB: Expert Parallelism Load Balancer
What it is
A Python library that implements load balancing algorithms for Mixture-of-Experts (MoE) models using expert parallelism. It computes optimal expert replication and placement plans across GPUs and nodes to minimize load imbalance and inter-node traffic.
The problem it solves
Uneven load distribution across GPUs in expert-parallel MoE deployments causes underutilization and performance bottlenecks.
What you could use it for
- Balancing expert load across GPUs in DeepSeek-V3 deployments
- Optimizing MoE model inference performance in multi-node clusters
- Implementing redundant expert strategies for large language models
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.