REDDIT · REDDIT// 27d 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

27d ago

2026-03-16

PUBLISHED

27d ago

2026-03-16

RELEVANCE

7/ 10

AUTHOR

bigattichouse