gemma 4 26B A4B it MLX 5bit

gemma

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

Released April 2026context N/A26B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 5-bit precision and packaged by lmstudio-community for local MLX inference on Apple Silicon. It handles both text and image inputs, making it capable of visual understanding tasks alongside text work. The quantization keeps memory footprint manageable while trading some fidelity compared to full-precision weights.

Capabilities

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

Reasoning & Logic

Moderate

Multilingual

Moderate

Instruction Following

Use Case Fit

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

gemma 4 26B A4B it MLX 5bit

gemma

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

Released April 2026context N/A26B params

A mid-sized multimodal model from Google's Gemma 4 family, quantized to 5-bit precision and packaged by lmstudio-community for local MLX inference on Apple Silicon. It handles both text and image inputs, making it capable of visual understanding tasks alongside text work. The quantization keeps memory footprint manageable while trading some fidelity compared to full-precision weights.

Capabilities

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

Reasoning & Logic

Moderate

Multilingual

Moderate

Instruction Following

Use Case Fit

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

Glossary

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.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Full-Precision WeightsModel parameters stored at maximum numerical accuracy (typically 32-bit floating point), which provides the best quality but requires more memory and computation.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.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.Visual UnderstandingThe ability of an AI model to interpret and analyze images, including identifying objects, reading text, and answering questions about visual content.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.