gemma 4 31B it MLX 4bit

gemma

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

Released April 2026context N/A31B params

A multimodal open-weight model that handles both text and image inputs, quantized to 4-bit precision for efficient local deployment via MLX. The 4-bit quantization makes it accessible on consumer hardware with reduced memory footprint, though this comes with some quality trade-offs compared to full-precision variants. It processes visual and textual information together, making it capable of tasks that require understanding images alongside written context.

Capabilities

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

Reasoning & Logic

Strong

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 4bit

gemma

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

Released April 2026context N/A31B params

A multimodal open-weight model that handles both text and image inputs, quantized to 4-bit precision for efficient local deployment via MLX. The 4-bit quantization makes it accessible on consumer hardware with reduced memory footprint, though this comes with some quality trade-offs compared to full-precision variants. It processes visual and textual information together, making it capable of tasks that require understanding images alongside written context.

Capabilities

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

Reasoning & Logic

Strong

Instruction Following

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.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.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.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.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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.