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

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	8.19B (~6.95B non-embedding)
Architecture	Qwen3-8B dense: GQA (32 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm
Layers	36 Transformer decoder blocks
Context length	65,536 tokens
Vocab size	151,936
Weight format	GGUF Q1_0
Deployed size	1.15 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	16.38 GB	—	1.0x
GGUF Q1_0	1.15 GB	93.0%	14.2x
MLX 1-bit g128	1.28 GB	92.2%	12.8x

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

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)

bash

# 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-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)

bash

# 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-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

bash

./build/bin/llama-server \
    -m Bonsai-8B-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	368	59	6.2x	11,809	10,453
RTX L40S	llama.cpp CUDA	327	52	6.3x	9,592	8,325
RTX 3060 Laptop	llama.cpp CUDA	81	3.5¹	23x¹	1,871	94¹
M4 Pro 48 GB	llama.cpp Metal	85	16	5.4x	498	490
Samsung S25 Ultra	llama.cpp OpenCL	19.6	—	—	30.4	—

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

Energy Efficiency

Platform	Bonsai E_tg (mWh/tok)	Baseline E_tg	Advantage
RTX 4090 (CUDA)	0.276	1.134 (FP16)	4.1x
Mac M4 Pro (Metal)	0.091	0.471 (FP16)	5.1x

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.

Model	Company	Size	Avg	MMLU-R	MuSR	GSM8K	HE+	IFEval	BFCL
Qwen 3 8B	Alibaba	16 GB	79.3	83	55	93	82.3	84.2	81
RNJ 8B	EssentialAI	16 GB	73.1	75.5	50.4	93.7	84.2	73.8	61.1
Mistral3 8B	Mistral	16 GB	71.0	73.9	53.8	87.2	67.4	75.4	45.4
Olmo 3 7B	Allen Inst	14 GB	70.9	72	56.1	92.5	79.3	37.1	38.4
1-bit Bonsai 8B	PrismML	1.15 GB	70.5	65.7	50	88	73.8	79.8	65.7
LFM2 8B	LiquidAI	16 GB	69.6	72.7	49.5	90.1	81	82.2	62.0
Llama 3.1 8B	Meta	16 GB	67.1	72.9	51.3	87.9	75	51.5	—
GLM v6 9B	ZhipuAI	16 GB	65.7	61.9	43.2	93.4	78.7	69.3	21.9
Hermes 8B	Nous Research	16 GB	65.4	67.4	52.2	82.9	51.2	65	73.5
Trinity Nano 6B	Arcee	12 GB	61.2	68.8	52.6	81.1	54	50	62.5
Marin 8B	Stanford CRFM	16 GB	56.6	64.8	42.6	86.4	51	50	—
R1-D 7B	DeepSeek	14 GB	55.1	62.5	29.1	92.7	81.7	48.8	15.4

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

Model	Size	Intelligence Density (1/GB)
1-bit Bonsai 8B	1.15 GB	1.062
Qwen 3 8B	16 GB	0.098
Llama 3.1 8B	16 GB	0.074
Mistral3 8B	16 GB	0.077

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

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:

bibtex

@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

jinreiyu/clone-Bonsai-8B-gguf

Bonsai-8B-GGUF-1bit

Highlights

Resources

Model Overview

Quantization Format: Q1_0

Memory Requirement

Best Practices

Generation Parameters

System Prompt

Quickstart

llama.cpp (CUDA)

llama.cpp (Metal / macOS)

llama.cpp Server

Cross-Platform Throughput

Energy Efficiency

Benchmarks

Intelligence Density

Use Cases

Limitations

Citation

Contact

jinreiyu/
clone-Bonsai-8B-gguf