moe-dispatcher-selection

What problem does it solve?

Efficiently selecting the optimal MoE token dispatcher (AlltoAll, DeepEP, or HybridEP) for a given hardware platform, EP degree, and workload, to maximize throughput and memory efficiency.

Core Features & Use Cases

• Guidance by hardware and EP degree to pick among AlltoAll, DeepEP, and HybridEP based on workload patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.
• Includes tuning recommendations and NVLink-domain considerations for H100, GB200, and GB300 systems to plan experiments and deployments.
• Use Case: data scientists and engineers tuning MoE workloads for large-scale language models and vision models across multi-GPU setups.

Quick Start

Provide a single instruction to evaluate hardware, EP degree, and workload characteristics to receive a recommended MoE dispatcher and baseline tuning options.