Benchmark Compares Ultra-Quantized Bonsai 2 27B Against Gemma and Qwen Models
A community evaluation benchmarked PrismML's heavily quantized Bonsai 2 27B (PQ2_0, 7.2 GB) against Gemma 4 12B (Q8_0, 12.7 GB) and Qwen 3.5 9B (Q6_K, 7.5 GB) on an Nvidia RTX 5090 for a 3D voxel scene generation task. Bonsai 2 significantly outperformed the competitors in spatial reasoning and scene detail despite consuming over 106,000 output tokens, with 90% dedicated to internal reasoning. The test demonstrates that ultra-low bit ternary quantization can pack high-level reasoning capabilities of 27B models into under 8 GB of VRAM, making advanced autonomous coding and 3D generation runnable on consumer GPUs like the RTX 3060. This highlights significant progress in deploying intelligent, memory-efficient models for local open-source AI workflows. Executed on a custom PrismML fork of llama.cpp with a 262,144-token context length, Bonsai 2 achieved ~101 tokens/second and generated a single-file Three.js Japanese pagoda, while Gemma's output was washed out and Qwen's output broke completely. A key technical requirement is that Bonsai's GGUF quantization currently requires PrismML's custom runtime fork rather than stock llama.cpp.
## BACKGROUND
Quantization reduces the precision of a model's weights to drastically lower VRAM footprint while attempting to preserve accuracy. PrismML specializes in 1-bit and ternary quantization techniques designed to pack large foundation models into memory-constrained hardware such as consumer laptops and mid-range graphics cards.