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
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