Model overview
Bonsai-8B-gguf is an 8-billion parameter language model compressed to extreme efficiency through 1-bit quantization. Built on the Qwen3-8B architecture, it achieves a deployed size of just 1.15 GB—14.2 times smaller than the full-precision FP16 version—while maintaining competitive performance across standard benchmarks. The model uses a GGUF Q1_0_g128 quantization format where each weight is represented as a single bit with shared scale factors across groups of 128 weights. This approach enables inference across CUDA, Metal, CPU, and Android platforms without materializing full-precision weights during computation.
Model inputs and outputs
Bonsai-8B-gguf accepts text prompts and generates coherent text continuations. The model operates with a 65,536-token context window, supporting both short interactions and extended document processing. Input text is tokenized using a 151,936-token vocabulary, and the model generates output one token at a time, with generation parameters controlling diversity and quality.
Inputs
- Text prompts: any natural language query or instruction
- Generation parameters: temperature (0.5–0.7 recommended), top-k (20–40), top-p (0.85–0.95), and repetition penalties
- System prompts: optional guidance for model behavior (e.g., "You are a helpful assistant")
Outputs
- Generated text: continuation or response to the input prompt
- Token sequences: up to 256 tokens per generation in typical use, adjustable based on needs
Capabilities
The model handles instruction-following, question-answering, summarization, and creative writing tasks. Despite extreme quantization, it matches full-precision 8B models on benchmarks like MMLU-R, MuSR, GSM8K, and IFEval, achieving an average score of 70.5 across six evaluation categories. Performance remains stable across different hardware platforms: it generates 368 tokens per second on an RTX 4090 (6.2x faster than FP16), 85 tokens per second on an M4 Pro Mac, and 19.6 tokens per second on mobile devices like the Samsung S25 Ultra. The model's 1-bit representation covers embeddings, attention projections, MLP layers, and the language modeling head.
What can I use it for?
On-device assistants benefit from the minimal footprint—deploy interactive AI on laptops and phones with single-digit millisecond latencies. Mobile applications gain a capable language model that fits in under 2 GB of memory, enabling features like offline chat, content generation, and real-time translation without cloud dependencies. Edge computing scenarios and battery-constrained environments see 4–5x lower energy consumption per token compared to full-precision models. Research teams exploring model compression can study the Whitepaper and Demo repo to understand extreme quantization techniques. Developers can also reference related work on 1-bit infrastructure and ultra-low-bit quantization.
Things to try
Start with the Google Colab notebook to test generation without local setup—no GPU required for exploration. Experiment with temperature settings between 0.5 and 0.7 to balance coherence and creativity; lower temperatures produce more predictable responses while higher values introduce more variation. Deploy the model as an HTTP server using llama.cpp to build scalable applications, or run it natively on Apple Silicon using the MLX variant for optimized Metal acceleration. Benchmark throughput on your target hardware (RTX, Mac, Android, or CPU) to understand real-world performance, as single-bit quantization delivers dramatically different speeds depending on the platform. Compare energy consumption and latency against full-precision alternatives to quantify efficiency gains for your use case.
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