Bonsai 8B mlx 1bit

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released March 202666K context≈ 49,152 words

Bonsai 8B mlx 1bit is a heavily compressed model designed to run efficiently on Apple Silicon via the MLX framework. The aggressive 1-bit quantization dramatically reduces memory footprint, making it deployable on consumer hardware, though this comes with noticeable quality trade-offs compared to full-precision counterparts. It suits experimentation and local inference where resource constraints matter more than peak output quality.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Moderate

Instruction Following

Bonsai 8B mlx 1bit

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released March 202666K context≈ 49,152 words

Bonsai 8B mlx 1bit is a heavily compressed model designed to run efficiently on Apple Silicon via the MLX framework. The aggressive 1-bit quantization dramatically reduces memory footprint, making it deployable on consumer hardware, though this comes with noticeable quality trade-offs compared to full-precision counterparts. It suits experimentation and local inference where resource constraints matter more than peak output quality.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Moderate

Instruction Following

Glossary

1-Bit QuantizationAn extreme form of compression that represents model weights using only 1 bit of information per value, drastically reducing memory use but with significant quality loss.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Local InferenceRunning an AI model directly on your own computer rather than sending data to a remote server, keeping data private and reducing latency.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MLX FrameworkA machine learning framework specifically designed for running AI models efficiently on Apple Silicon hardware.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.

Capabilities

Capabilities

Use Case Fit

Glossary