gemma 4 12B it 4bit

Gemma

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

Released June 2026context N/A12B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 4-bit precision by the MLX community for efficient local inference on Apple Silicon. The 4-bit quantization reduces memory footprint significantly, making it runnable on consumer hardware, though with some quality trade-off compared to full-precision versions. It handles both text and image inputs, offering a practical balance between capability and resource use.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

Strong

Factual Knowledge

Use Case Fit

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

gemma 4 12B it 4bit

Gemma

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

Released June 2026context N/A12B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 4-bit precision by the MLX community for efficient local inference on Apple Silicon. The 4-bit quantization reduces memory footprint significantly, making it runnable on consumer hardware, though with some quality trade-off compared to full-precision versions. It handles both text and image inputs, offering a practical balance between capability and resource use.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

Strong

Factual Knowledge

Use Case Fit

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

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.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.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.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.