AI is happening. The future is updating.Bonsai-8B-gguf
Prism ML Website | Whitepaper | Demo & Examples | Colab Notebook | Discord > **14.1x** smaller than FP16 | **6.2x** faster on RTX 4090 | **4-5x** lower energy/token - **1.15 GB** parameter memory (down from 16.38 GB FP16) — fits on virtually any device with a GPU - **End-to-end 1-bit weights** across embeddings, attention projections, MLP projections, and LM head - **GGUF Q1_0 (g128)** format with inline dequantization kernels — no FP16 materialization - **Cross-platform**: CUDA (RTX/datacenter), Metal (Mac), Android, CPU - **Competitive benchmarks**: 70.5 avg score across 6 categories, matching full-precision 8B models at 1/14th the size - **MLX companion**: also available as MLX 1-bit g128 for native Apple Silicon inference
Prism ML Website | Whitepaper | Demo & Examples | Colab Notebook | Discord > **14.1x** smaller than FP16 | **6.2x** faster on RTX 4090 | **4-5x** lower energy/token - **1.15 GB** parameter memory (down from 16.38 GB FP16) — fits on virtually any device with a GPU - **End-to-end 1-bit weights** across embeddings, attention projections, MLP projections, and LM head - **GGUF Q1_0 (g128)** format with inline dequantization kernels — no FP16 materialization - **Cross-platform**: CUDA (RTX/datacenter), Metal (Mac), Android, CPU - **Competitive benchmarks**: 70.5 avg score across 6 categories, matching full-precision 8B models at 1/14th the size - **MLX companion**: also available as MLX 1-bit g128 for native Apple Silicon inference