Bonsai-8B-gguf

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Bonsai-8B-GGUF-1bit

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

14.1x smaller than FP16 | 6.2x faster on RTX 4090 | 4-5x lower energy/token

Highlights

• 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

<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

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):

The GGUF file on disk is 1.16 GB (~6.6 MB larger) because the format embeds the tokenizer, chat template, and model metadata alongside the weights.

Best Practices

Generation Parameters

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-8B.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-8B.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-8B.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

¹ FP16 only fits partially on GPU's 6 GB VRAM; 1-bit fits entirely in VRAM.

<p align="center"> <img src="./assets/speeds_8B.png" width="680" alt="Cross-platform throughput"> </p>

Energy Efficiency

<p align="center"> <img src="./assets/energy_8B.png" width="680" alt="Energy efficiency"> </p>

Benchmarks

Evaluated with EvalScope v1.4.2 + vLLM 0.15.1 on NVIDIA H100 under identical infrastructure, generation parameters, and scoring. All models are in the 6B–9B parameter range.

Despite being 1/14th the size, 1-bit Bonsai 8B is competitive with leading full-precision 8B instruct models.

Intelligence Density

Intelligence density captures the ratio of a model's capability to its deployed size:

alpha = -ln(1 - score/100) / size_GB

Bonsai 8B achieves 10.8x higher intelligence density than full-precision Qwen 3 8B.

<p align="center"> <img src="./assets/intel_density_8B.png" width="680" alt="Intelligence density"> </p>

Use Cases

• On-device assistants: interactive AI on laptops and phones with low latency
• Mobile deployment: runs on a wide variety of phones due to low memory footprint
• Edge robotics and autonomy: compact deployment on devices with thermal, memory, or connectivity constraints
• Cost-sensitive GPU serving: higher throughput and lower energy per token on RTX-class and datacenter GPUs
• Enterprise and private inference: local or controlled-environment inference for data residency requirements

Limitations

• No native 1-bit hardware exists yet — current gains are software-kernel optimizations on general-purpose hardware
• Mobile power measurement is estimated rather than hardware-metered
• The full-precision benchmark frontier continues to advance; the 1-bit methodology is architecture-agnostic and will be applied to newer bases

Citation

If you use 1-bit Bonsai 8B, please cite:

@techreport{bonsai8b,
    title   = {1-bit Bonsai 8B: 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: [email protected]