PrismML · Ternary Quantisation

Ternary Bonsai 2 27B

Qwen3.8 27B quantised to ternary weights, meaning each weight is -1, 0 or +1 with FP16 group-wise scaling, landing at 1.76 effective bits per weight and a 5.9GB footprint, over nine times smaller than full precision. It keeps the 262K context and multimodal text-and-image input. PrismML's own comparison against full-precision Qwen3.8 27B puts the aggregate at 83.9 against 85.4, or 98.2% of full-precision performance, with 81.58 against 82.17 on coding, 96.57 against 97.06 on maths, 78.59 against 81.64 on vision and 77.57 against 79.74 on agentic tasks. Apache 2.0 with weights out now, running through custom low-bit kernels on NVIDIA GPUs and Apple devices.