gemma 4 E2B it MLX 8bit

gemma

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

Released April 2026context N/A

A compact, quantized text model running in 8-bit precision via the MLX framework, optimized for Apple Silicon hardware. It trades some numerical precision for reduced memory footprint, making it practical to run locally on machines that might otherwise struggle with full-precision weights. Behavior stays close to the base Gemma 4 family, with the quantization introducing minimal but present degradation.

Capabilities

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

Creative Writing

Moderate

Instruction Following

Moderate

Factual Knowledge

Use Case Fit

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

gemma 4 E2B it MLX 8bit

gemma

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

Released April 2026context N/A

A compact, quantized text model running in 8-bit precision via the MLX framework, optimized for Apple Silicon hardware. It trades some numerical precision for reduced memory footprint, making it practical to run locally on machines that might otherwise struggle with full-precision weights. Behavior stays close to the base Gemma 4 family, with the quantization introducing minimal but present degradation.

Capabilities

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

Creative Writing

Moderate

Instruction Following

Moderate

Factual Knowledge

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.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.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.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 ModelA language model that processes and generates only text, without support for images, audio, or other media types.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.