gemma 4 E4B it MLX 6bit

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 6-bit precision and packaged by lmstudio-community for local MLX inference. The reduced bit-width keeps memory footprint small at the cost of some precision compared to full-weight variants. Behaves as a general text-in, text-out model suited for running on Apple Silicon hardware via MLX.

Capabilities

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

Factual Knowledge

Moderate

Reasoning & Logic

Moderate

Long Context

Use Case Fit

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

gemma 4 E4B it MLX 6bit

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 6-bit precision and packaged by lmstudio-community for local MLX inference. The reduced bit-width keeps memory footprint small at the cost of some precision compared to full-weight variants. Behaves as a general text-in, text-out model suited for running on Apple Silicon hardware via MLX.

Capabilities

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

Factual Knowledge

Moderate

Reasoning & Logic

Moderate

Long Context

Use Case Fit

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

Glossary

6-bit PrecisionA quantization method that represents model weights using only 6 bits per value, significantly reducing memory requirements compared to standard 32-bit floating-point storage.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.Bit-WidthThe number of bits used to represent each number in a model; lower bit-widths (like 6-bit) use less memory but may reduce precision compared to higher bit-widths.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.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.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.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.