---

license: apache-2.0

library_name: llama.cpp

pipeline_tag: text-generation

tags:

• 1-bit
• gguf
• llama-cpp
• cuda
• metal
• on-device
• prismml
• bonsai

base_model:

• prism-ml/Bonsai-4B-unpacked

---

<p align="center">

<img src="./assets/bonsai-logo.svg" width="280" alt="Bonsai">

</p>

<p align="center">

<a href="https://prismml.com"><b>Prism ML Website</b></a> &nbsp;|&nbsp;

<a href="https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-bonsai-8b-whitepaper.pdf"><b>Whitepaper</b></a> &nbsp;|&nbsp;

<a href="https://github.com/PrismML-Eng/Bonsai-demo"><b>Demo &amp; Examples</b></a> &nbsp;|&nbsp;

<a href="https://colab.research.google.com/drive/1EzyAaQ2nwDv_1X0jaC5XiVC3ZREg9bdG?usp=sharing"><b>Colab Notebook</b></a> &nbsp;|&nbsp;

<a href="https://discord.gg/prismml"><b>Discord</b></a>

</p>

Bonsai-4B-GGUF-1bit

End-to-end 1-bit language model for llama.cpp (CUDA, Metal, CPU)

> 14.1x smaller than FP16 | 4.2x faster on RTX 4090 | runs on any device

Highlights

• Deployed footprint 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 for 1-bit packing of weights with shared scales for each group (group size 128).
• Cross-platform: CUDA (RTX/datacenter), Metal (Mac), Swift (iPhone/iPad), Android
• MLX companion: also available as MLX 1-bit g128 for native Apple Silicon inference

<p align="center">

<img src="./assets/frontier.svg" width="680" alt="Frontier Efficiency">

</p>

Resources

• Google Colab — try Bonsai in your browser, no setup required
• Whitepaper — for more details on Bonsai, check out our whitepaper
• Demo repo — comprehensive examples for serving, benchmarking, and integrating Bonsai
• Discord — join the community for support, discussion, and updates
• 1-bit kernels: llama.cpp fork (CUDA + Metal) · MLX fork (Apple Silicon) · mlx-swift fork (iOS/macOS)
• Locally AI — we have partnered with Locally AI for iPhone support

Model Overview

| Item | Specification |

| :------------- | :--------------------------------------------------------------------- |

| Parameters | 4.0B (~3.6B non-embedding) |

| Architecture | Qwen3-4B dense: GQA (32 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm |

| Layers | 36 Transformer decoder blocks |

| Context length | 32,768 tokens |

| Vocab size | 151,936 |

| Weight format | GGUF Q1_0

| Deployed size | 0.57 GB (14.2x smaller than FP16) |

| 1-bit coverage | Embeddings, attention projections, MLP projections, LM head |

| License | Apache 2.0 |

Quantization Format: Q1_0

Each weight is a single bit: 0 maps to −scale, 1 maps to +scale. Every group of 128 weights shares one FP16 scale factor.

Effective bits per weight: 1.125 (1 sign bit + 16-bit scale amortized over 128 weights).

Memory Requirement

Parameter memory only (weights and scales loaded into memory):

| Format | Size | Reduction | Ratio |

| :----------------- | ----------: | --------: | --------: |

| FP16 | 8.04 GB | — | 1.0x |

| GGUF Q1_0 | 0.57 GB | 93.0% | 14.2x |

| MLX 1-bit g128 | 0.63 GB | 92.2% | 12.8x |

The GGUF file on disk is 0.57 GB (~6.4 MB larger) because the format embeds the tokenizer, chat template, and model metadata alongside the weights.

Best Practices

Generation Parameters

| Parameter | Default | Suggested range |

| :----------------- | :------ | :-------------- |

| Temperature | 0.5 | 0.5 -- 0.7 |

| Top-k | 20 | 20 -- 40 |

| Top-p | 0.9 | 0.85 -- 0.95 |

| Repetition penalty | 1.0 | |

| Presence penalty | 0.0 | |

System Prompt

You can use a simple system prompt such as:

You are a helpful assistant

Quickstart

llama.cpp (CUDA)

# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)
git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp

# Build with CUDA support
cmake -B build -DGGML_CUDA=ON && cmake --build build -j

# Run inference
./build/bin/llama-cli \
    -m Bonsai-4B-Q1_0.gguf \
    -p "Explain quantum computing in simple terms." \
    -n 256 \
    --temp 0.5 \
    --top-p 0.85 \
    --top-k 20 \
    -ngl 99

llama.cpp (Metal / macOS)

# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)
git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp

# Build with Metal support (default on macOS)
cmake -B build && cmake --build build -j

# Run inference
./build/bin/llama-cli \
    -m Bonsai-4B-Q1_0.gguf \
    -p "Explain quantum computing in simple terms." \
    -n 256 \
    --temp 0.5 \
    --top-p 0.85 \
    --top-k 20 \
    -ngl 99

llama.cpp Server

./build/bin/llama-server \
    -m Bonsai-4B-Q1_0.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 99

Open the web UI at http://127.0.0.1:8080, or see our llama.cpp fork for more examples.

Cross-Platform Throughput

| Platform | Backend | TG128 (tok/s) | FP16 TG (tok/s) | TG vs FP16 | PP512 (tok/s) | FP16 PP512 (tok/s) |

| :----------- | :-------------- | ------------: | --------------: | ---------: | ------------: | -----------------: |

| RTX 4090 | llama.cpp CUDA | 440 | 105 | 4.2x | 18,135 | 16,310 |

| M4 Pro 48 GB | llama.cpp Metal | 136 | 29 | 4.7x | 915 | 915 |

Citation

If you use 1-bit Bonsai 4B, please cite:

@techreport{bonsai,
    title   = {Bonsai: End-to-End 1-bit Language Model Deployment
               Across Apple, GPU, and Mobile Runtimes},
    author  = {Prism ML},
    year    = {2026},
    month   = {March},
    url     = {https://prismml.com}
}

Contact

For questions, feedback, or collaboration inquiries: contact@prismml.com