A new open-source project, an experimental load balancer for Mixture-of-Experts (MoE) models.
The repository describes how the system:
• dynamically redistributes experts based on load statistics;
• creates replicas considering cluster topology;
• solves the optimal token distribution across experts using an LP solver running directly on the GPU (cuSolverDx + cuBLASDx);
• uses load metrics obtained manually, via torch.distributed, or through Deep-EP buffers.
Guide shows what a smart and precise load balancer for large MoE architectures might look like.
GitHub
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