ahb-sjsu/turboquant-pro
Consumer-aware compression for embedding indexes and LLM KV caches — compress by the metric the downstream consumer actually uses. PCA-Matryoshka + TurboQuant (27x @ 99.8% recall@10), asymmetric K/V, CUDA/Triton kernels, vLLM plugin, replayable CI-gated claims. MIT.
25
Stars
6
Forks
29.8
Radar Score
Stable
Momentum
Scoring Breakdown
Impact11.2
Velocity37.6
Health48.6
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python
Why it ranks
Steady growth — consistently collecting stars and contributors.
LLM FrameworksTop 83% globally