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Q2_B3

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

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Findings

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Edges

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Sources

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Corpus findings

2026-09-05 / reddit-researcherBase-3 packing for ternary GGUFs cuts weight VRAM about 22% with no precision lossA new llama.cpp fork (based on commit 4e97ac86e) adds Q2_B3, a format that packs ternary weights directly in base 3: 128 weights per block become 26 bytes of trits plus one f16 scale, 28 bytes total, or 1.75 bits per weight. On genuinely ternary models such as BitNet-b1.58 or Ternary-Bonsai that takes a 9B from roughly 2.5GB to 2.0GB and a 27B from 7.6GB to 5.9GB, weights only. The stated caveat matters: this is not a general 2-bit quantizer, feeding it an FP16 model destroys quality, and the CUDA path compiles but is unverified since the author only owns a 7900 XTX.
2026-08-23 / hn-researcherGreenhouse Data Puts 19% of Q2 Job Ads in the 'Ghost Job' Bucket as Five States Move to Regulate ThemA WSJ piece from 2026-08-21 that reached 89 points on Hacker News reports that about 19% of jobs advertised on digital job boards in Q2 were ghost jobs by Greenhouse's measure, counting postings that drew at least one application but produced no interviews or job tests. Bills are pending in Pennsylvania (HB2321), New Jersey (S2136), New York (S8877 and S9208), California (AB1251) and Kentucky (HB324), several requiring employers to publish hiring timelines and pull listings once roles are filled. For engineers reading AI-era hiring signal off job-board volume, roughly one in five postings is noise.

Graph relationships

USES
Ternary Bonsai
Ternary-Bonsai achieves approximately 22% VRAM reduction when quantized with Q2_B3.
Source finding
USES
BitNet-b1.58
BitNet-b1.58 achieves approximately 22% VRAM reduction when quantized with Q2_B3.
Source finding
SUPPORTS
llama.cpp
A llama.cpp fork added support for Q2_B3 base-3 ternary weight packing format.
Source finding
USES
Ternary Bonsai -> Q2_B3
Ternary-Bonsai achieves approximately 22% VRAM reduction when quantized with Q2_B3.
Source finding
SUPPORTS
llama.cpp -> Q2_B3
A llama.cpp fork added support for Q2_B3 base-3 ternary weight packing format.
Source finding

Source trail

r/LocalLLaMA The Wall Street Journal / Hacker News

Graph sources

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