gemma 4 31B it MLX 5bit

gemma

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

Released April 2026context N/A31B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 5-bit precision for efficient local deployment via MLX. It handles both text and image inputs, making it capable of visual understanding tasks alongside text generation. The quantization trade-off means slightly reduced precision compared to full-weight versions, but enables faster inference on Apple Silicon hardware.

Capabilities

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

Tool Use

Moderate

Factual Knowledge

Moderate

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

gemma 4 31B it MLX 5bit

gemma

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

Released April 2026context N/A31B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 5-bit precision for efficient local deployment via MLX. It handles both text and image inputs, making it capable of visual understanding tasks alongside text generation. The quantization trade-off means slightly reduced precision compared to full-weight versions, but enables faster inference on Apple Silicon hardware.

Capabilities

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

Tool Use

Moderate

Factual Knowledge

Moderate

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Text GenerationThe process of an AI model creating new text one word or token at a time based on patterns it learned during training.Visual UnderstandingThe ability of an AI model to interpret and analyze images, including identifying objects, reading text, and answering questions about visual content.