1 Bit Bonsai 4B
1-bit Bonsai 4B is PrismML’s mid-size ultra-efficient model designed for fast local inference. It uses just 0.57GB of memory and is positioned as a strong balance of speed, accuracy, and energy efficiency, with PrismML reporting throughput of 132 tokens per second on an M4 Pro for workloads that need both responsiveness and capable language performance.
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1 Bit Bonsai 1.7B
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1-bit Bonsai 1.7B is PrismML’s smallest Bonsai model, built for extremely fast on-device inference. With a me…
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1 Bit Bonsai 8B
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1-bit Bonsai 8B is PrismML’s flagship ultra-efficient language model, built with 1-bit weights for edge and r…
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1-bit Bonsai Image 4B
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1-bit Bonsai Image 4B is PrismML’s open-weight compressed image-generation model built for local diffusion in…
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Bonsai 27B
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Bonsai 27B is a 27.8B-parameter multimodal language model built on Qwen3.6 27B, quantized end-to-end to 1-bit…
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Bonsai 27B AWQ 4bit
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Bonsai 27B AWQ 4bit is a 4-bit AWQ quantized release of Prism ML's Bonsai 27B, a 27 billion parameter multimo…
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Bonsai 27B mlx 1bit
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1-bit quantized version of the 27B-parameter Qwen3.6-27B language model, using binary g128 weight representat…
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Ternary Bonsai 27B
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A 27B parameter language model compressed to ternary (1.71 bit) weights, retaining 95% of full precision inte…
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Ternary Bonsai 27B AWQ 4bit
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Ternary Bonsai 27B AWQ 4bit is a 4 bit AWQ quantized build of PrismML's Ternary Bonsai 27B, a multimodal mode…
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