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Mixture-of-experts routing

DeepSpeed-MoE

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Superseded baseline#9 of 1,370 most-superseded · first seen Jan 14, 2022

Superseded — cited as a baseline and beaten by newer methods

4 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites DeepSpeed-MoE as a baseline.

existing MoE training systems, such as DeepSpeed-MoE~deepspeed-moe, DeepSpeed-TED~deepspeed-ted, and Tutel~hwang2023tutel, do not effectively address this shifted bottleneck, causing a memory explosion
— X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms
“its reliance on expert parallelism with static assignment makes it less effective for dynamic and unbalanced workloads”
— Accelerating MoE Model Inference with Expert Sharding
“their current capabilities are limited to manual configuration of the pipeline degree or heuristic search methods within a constrained search space”
— FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
“A general limitation across these frameworks is the absence of platform-aware hybrid parallelism planning that jointly accounts for memory, compute, and communication constraints, a gap Piper directly addresses”
— Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism

Beaten on benchmarks

Head-to-head results where a newer method reports beating DeepSpeed-MoE. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.