OPEN LINK ↗
// 72d agoOPENSOURCE RELEASE
Adaptive-Quantization brings SNR-based, per-tensor llama.cpp quantization
A new open-source toolkit replaces llama.cpp's opaque Q4_K_M-style labels with filenames like `Qwen3.5-9B_12.6GB_45dB` that expose actual file size and signal-to-noise ratio. Scripts survey every tensor at every quantization level to produce mixed-precision GGUF files optimized for either a quality floor or a VRAM budget.
// ANALYSIS
The QX_Y naming scheme has always been a proxy for quality — this project cuts through the abstraction by measuring what actually matters.
- –Per-tensor SNR profiling can yield models 3–6% smaller than uniform quantization at equivalent quality, a meaningful win for local inference
- –Filename convention `ModelName_SizeGB_SNRdB` gives practitioners instant, comparable quality signals without running benchmarks
- –Target-driven quantization (`--db 30` for a quality floor or `--size 8G` for a VRAM budget) flips the workflow: specify constraints, get the optimal plan
- –`verify_gguf.py` closes the loop with post-quantization validation so users know the achieved SNR, not just the planned one
- –Community traction is early (score 1 on r/LocalLLaMA), but the idea directly addresses a recurring pain point in the local-LLM space
// TAGS
adaptive-quantizationllminferenceopen-sourcedevtooledge-ai
DISCOVERED
72d ago
2026-03-16
PUBLISHED
72d ago
2026-03-16
RELEVANCE
7/ 10
AUTHOR
bigattichouse