gemma 4 E4B it MLX 8bit

gemma

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

Released April 2026context N/A

A compact, open-weight model from Google's Gemma 4 family, quantized to 8-bit precision for efficient local inference via MLX. The 8-bit quantization keeps memory footprint manageable while preserving much of the original model's capability, though some precision loss is expected compared to full-weight versions. It handles text-in, text-out tasks and runs well on Apple Silicon hardware through the MLX format.

Capabilities

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

Multilingual

Moderate

Long Context

Moderate

Creative Writing

Use Case Fit

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

gemma 4 E4B it MLX 8bit

gemma

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

Released April 2026context N/A

A compact, open-weight model from Google's Gemma 4 family, quantized to 8-bit precision for efficient local inference via MLX. The 8-bit quantization keeps memory footprint manageable while preserving much of the original model's capability, though some precision loss is expected compared to full-weight versions. It handles text-in, text-out tasks and runs well on Apple Silicon hardware through the MLX format.

Capabilities

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

Multilingual

Moderate

Long Context

Moderate

Creative Writing

Use Case Fit

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

Glossary

8-bit PrecisionA quantization method that represents model weights using 8 bits instead of the standard 32 bits, reducing memory usage by approximately 75% while maintaining reasonable performance.8-bit QuantizationA specific quantization method that represents model weights using 8 bits instead of the standard 32 bits, significantly reducing memory requirements.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 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 FormatA model format designed specifically for efficient inference on Apple Silicon devices, optimized for the MLX machine learning framework.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.Precision LossThe reduction in numerical accuracy that occurs when a model is compressed, which can slightly degrade performance on complex reasoning tasks while remaining acceptable for most everyday uses.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-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.