MI
EfficientMoE/MoE-Infinity
PyTorch library for cost-effective, fast and easy serving of MoE models.
293 25 +2/wk
GitHub
huggingface inference-engine large-language-models llm-inference mixture-of-experts pytorch
Trend
3
Star & Fork Trend (17 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
EfficientMoE/MoE-Infinity has +2 stars this period . 7-day velocity: 0.7%.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | MoE-Infinity | mcp_massive | hongbomiao.com | affiliate-skills |
|---|---|---|---|---|
| Stars | 293 | 293 | 293 | 294 |
| Forks | 25 | 82 | 50 | 125 |
| Weekly Growth | +2 | +1 | +0 | +4 |
| Language | Python | Python | Python | HTML |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | MIT | MIT | MIT |
Capability Radar vs mcp_massive
MoE-Infinity
mcp_massive
Maintenance Activity 100
Last code push 1 days ago.
Community Engagement 43
Fork-to-star ratio: 8.5%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 81
+2 stars this period — 0.68% growth rate.
License Clarity 95
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.