laekov/fastmoe
A fast MoE impl for PyTorch observed · 2026-08-28
Health v2 · maintenance only
26/100
- Activity 6
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2047
- days_rel: n/a
- days_push: 569
- n_releases_24m: 0
Adoption not part of the score
1859 stars · 206 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
FastMoE is a PyTorch library providing efficient Mixture of Experts (MoE) layers with custom C/CUDA operators. It supports distributed expert parallelism and one-key conversion of Transformer models (e.g., Megatron-LM) into MoE models.
Use cases
- scale up transformer MLP layers to mixture of experts
- train MoE models in PyTorch
- run expert parallel training across GPUs
- convert Megatron-LM models to MoE
- implement sparse expert routing in deep learning models
When to avoid
- you need MoE on CPU or without CUDA
- you use a framework other than PyTorch
- you want a maintained high-level API without custom CUDA builds
Facets
library · maturity active
machine-learning llm-training gpu-computing deep-learning large-language-models machine-learning python mixture-of-experts pytorch distributed-training transformer cuda gpu linux docker
1 source
- readme: https://github.com/laekov/fastmoe · fetched 2026-08-28 · 6dee94d44deb
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| laekov/fastmoe | main | 26 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem