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PrismML launches 1.58-bit model Ternary Bonsai, with parameters reduced by 9 times, surpassing peers in intelligence
The so-called 1.58-bit model refers to limiting the weights in neural networks to three values: {-1, 0, +1}. Compared to the previous ultra-compressed 1-bit models (weights only {-1, +1}), introducing the "0" value can effectively eliminate redundant connections, allowing the model to retain complex reasoning capabilities at a very small size.
The released Ternary Bonsai 8B weight file is only 1.75 GB, with an average benchmark score of 75.5, not only 5 points higher than their own 1-bit version but also significantly leading in "intelligent density" (performance contribution per GB of VRAM) over similar dense models like Qwen3.
Energy efficiency and speed are another core advantages of this series. On the iPhone 17 Pro Max, the 8B version can run at 27 tokens/sec, with an energy efficiency improvement of about 3 to 4 times.
For developers needing to deploy high-performance AI on mobile, laptop, and other edge devices, this means achieving near-full-precision model intelligence with minimal memory cost.
Currently, the Ternary Bonsai models are natively supported on Apple devices via the MLX framework. Model weights are distributed under the Apache 2.0 license.
(Source: BlockBeats)