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Bonsai is a family of open-weight models from PrismML. Instead of the 16-bit weights most models ship with, Bonsai models are quantized to 1 bit (weights in {−1, +1}) or ternary (weights in {−1, 0, +1} ). The practical consequence: the 1-bit Bonsai 27B model fits in 3.8GB versus 54GB for a full-precision model and the 1-bit Bonsai 8B model fits in 1.16 GB versus 16.38 GB for the same architecture in FP16. That is a 14x reduction, which is what lets Bonsai run on hardware that could never load a full-precision model of the same class.

Bonsai (1-bit)

Weights packed to a single bit each {−1, +1}. The smallest and fastest family.

Ternary-Bonsai (1.58-bit)

Weights take one of three values {−1, 0, +1}. Roughly 50% more the size of 1-bit and measurably higher quality.

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Why low-bit models

  • They run where full-precision models can’t. 1-bit Bonsai 27B needs only 3.8GB. It loads and runs on an iPhone 17 Pro, while leaving some memory for KV Cache. A 16-bit 27B model needs 54GB for the weights alone.
  • They generate faster. Token generation is memory-bandwidth-bound. Moving 1 to 1.58 bits per weight instead of 16 means each token requires a fraction of the memory traffic, so decode speed scales up accordingly on the same hardware.
  • They use less energy. Measured at roughly 4 to 5x better energy per token than 16-bit counterparts (0.068 mWh/tok for 1-bit 8B on an iPhone 17 Pro Max versus 0.132 mWh/tok for 4-bit 8B model).
  • They keep data local. Inference runs entirely on your machine. Nothing leaves it.
The speed and memory advantages depend on the runtime having native low-bit kernels. A runtime without them may dequantize the weights and silently lose the benefit. See Formats & runtime support before picking a tool.

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The lineup

Four sizes, each in both families, each under Apache-2.0:
ModelParametersContext1-bit on disk (GGUF)Ternary on disk (GGUF)
Bonsai 27B27B262,144 tokens3.9 GB7.2 GB
Bonsai 8B8.2B65,536 tokens1.16 GB2.18 GB
Bonsai 4B~4B32,768 tokens0.57 GB1.07 GB
Bonsai 1.7B~1.7B32,768 tokens0.25 GB0.46 GB
Bonsai 27B is the flagship and adds multimodal vision input. See the model overview for how to choose.

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Where to go next

Quickstart

Clone the demo repo and chat with Bonsai locally in one command.

Try it without installing

Hosted GPU demo, in-browser WebGPU, Colab, and iOS apps.

Pick a model

Sizes, families, benchmarks, and what fits your hardware.

How the quantization works

What 1-bit and 1.58-bit actually mean, layer by layer.

Run locally

llama.cpp, MLX, the OpenAI-compatible server, and Open WebUI.

Integrations

Connect Cline, LM Studio, Ollama, or Hermes.