# MoonshotAI/MoonEP

MoonEP: A Perfectly Balanced Expert Parallelism Library via Dynamic Redundant Experts

Repository: https://github.com/MoonshotAI/MoonEP
Canonical: https://ross.abutalabs.com/products/moonep
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-13T09:30:40+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 2
- inputs: {"age_days": 40, "days_push": 20, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1101, forks 122 (observed 2026-08-28T04:03:35.749686+00:00)

## What it is
MoonEP is an Expert Parallelism communication library for Mixture-of-Experts training that keeps token loads perfectly balanced across ranks using dynamically planned redundant experts. It features zero-copy token dispatch, static buffer shapes, and online GPU planning, outperforming DeepEP v2 under skewed routing.

## Use cases
- train MoE models without load imbalance slowing down ranks
- avoid OOM from dynamic activation shapes in expert parallel training
- speed up dispatch/combine communication for expert parallelism
- benchmark expert parallelism communication against DeepEP
- run MoE training on H20 GPUs with EP=8

## When to choose
- training large MoE models where router imbalance degrades throughput
- you need static memory shapes to avoid fragmentation and OOMs
- you want faster dispatch/combine than DeepEP under imbalance

## When to avoid
- you don't use Mixture-of-Experts architectures
- you need non-NVIDIA hardware (Zhenwu PPU support is incomplete)
- you need a mature, widely-adopted library with broad community support

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-training, gpu-computing, concurrency
- domain: machine-learning, deep-learning, large-language-models, gpu-computing, microservices
- platform: python
- tags: expert-parallelism, mixture-of-experts, moe, communication-library, load-balancing, training-infrastructure, nvidia-gpu, linux, gpu

## Member repositories
- MoonshotAI/MoonEP (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.749686+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:45:41.202410+00:00, confidence not recorded.
  - readme: https://github.com/MoonshotAI/MoonEP (fetched 2026-08-28T04:03:35.749686+00:00, sha 73c60eee12b0)
- Data as of 2026-08-30T08:39:29.467469+00:00.