Base-3 packing for ternary GGUFs cuts weight VRAM about 22% with no precision loss
r/LocalLLaMA·medium signal
A 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.