aivrar/multi-turboquant
Unified KV-cache compression for LLM inference: 12 Python-native methods, guarded add-on composition and routing, analytical capacity simulation, Godzilla KVarN/TriAttention with SM86/SM89 qualification, CUDA weight sharing, and multi-GPU planning.
25
Stars
6
Forks
37.0
Radar Score
Rising
Momentum
Scoring Breakdown
Impact15.5
Velocity41.5
Health65.0
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python
Why it ranks
Steady growth — consistently collecting stars and contributors.
AI Infrastructure · model servingTop 67% globally
Popular Alternatives & Competitors
Top projects in the same category (AI Infrastructure) — compare side by side.
🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
A
Based on maintenance, issue response, and release cadence.
Radar Score
37.0
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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