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Bonsai-8B-mlx-1bit

End-to-end 1-bit language model for Apple Silicon

12.8x smaller than FP16 | 8.4x faster on M4 Pro | 44 tok/s on iPhone | runs on Mac, iPhone, iPad

Highlights

• 1.28 GB parameter memory (down from 16.38 GB FP16) — runs comfortably on any Mac or iPhone
• End-to-end 1-bit weights across embeddings, attention projections, MLP projections, and LM head
• MLX-native format (1-bit g128) with inline dequantization kernels — no FP16 materialization
• Competitive benchmarks: 70.5 avg score across 6 categories, matching full-precision 8B models at 1/14th the size
• Cross-platform companion: also available as GGUF Q1_0_g128 for llama.cpp

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: MLX fork (Apple Silicon) · mlx-swift fork (iOS/macOS) · llama.cpp fork (CUDA + Metal)
• Locally AI — we have partnered with Locally AI for iPhone support

Model Overview

Quantization Format: 1-bit g128

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

MLX's quantization formats generally store both a scale and a bias per group: w = mlx_scale * bit + mlx_bias. To pack our scale-only 1-bit weights into this format:

mlx_scale = 2 * original_scale
mlx_bias  = −original_scale

This reconstructs −scale when bit=0 and +scale when bit=1. Because MLX stores two FP16 values per group (scale + bias) instead of one, the effective bits per weight is slightly higher than the GGUF format:

• MLX 1-bit g128: 1.25 bpw (1 sign bit + two 16-bit values amortized over 128 weights)
• GGUF Q1_0_g128: 1.125 bpw (1 sign bit + one 16-bit scale amortized over 128 weights)

Memory Requirement

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

The model directory on disk is ~1.30 GB (~16 MB larger) because it also includes tokenizer, config, and other metadata files alongside the weights.

Best Practices

Generation Parameters

System Prompt

You can use a simple system prompt such as:

You are a helpful assistant

Quickstart

MLX (Python)

Requires PrismML fork of MLX with 1-bit kernel support (upstream PR pending):

pip install mlx-lm
pip install mlx @ git+https://github.com/PrismML-Eng/mlx.git@prism

from mlx_lm import load, generate

model, tokenizer = load("prism-ml/Bonsai-8B-mlx-1bit")

response = generate(
    model,
    tokenizer,
    prompt="Explain quantum computing in simple terms.",
    max_tokens=256,
)
print(response)

MLX Swift (iOS / macOS)

1-bit Bonsai 8B runs natively on iPhone and iPad via MLX Swift at 44 tok/s on iPhone 17 Pro Max. Requires our mlx-swift fork with 1-bit kernels (upstream PR pending).

Throughput (MLX / Apple Silicon)

iPhone 17 Pro Max (MLX Swift)

FP16 does not fit on-device; baseline is 4-bit.

Energy Efficiency

Higher instantaneous power does not preclude lower energy — token generation is so much faster that energy per output token drops 4–6x.

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.

Use Cases

• On-device assistants: interactive AI on Mac, iPhone, and iPad with low latency and strong privacy
• 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 commodity GPU deployments
• 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 (Xcode Power Profiler) 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: contact@prismml.com