gemma 4 31B it MLX 8bit

gemma

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

Released April 2026context N/A

A mid-sized multimodal model that handles both text and image inputs, running in a quantized 8-bit MLX format optimized for Apple Silicon. The quantization keeps memory footprint manageable while preserving much of the original model's capability. It reflects Google's Gemma 4 architecture, repackaged by the LM Studio community for local deployment.

Capabilities

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

Creative Writing

Moderate

Multilingual

Moderate

Multimodal

Use Case Fit

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

gemma 4 31B it MLX 8bit

gemma

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

Released April 2026context N/A

A mid-sized multimodal model that handles both text and image inputs, running in a quantized 8-bit MLX format optimized for Apple Silicon. The quantization keeps memory footprint manageable while preserving much of the original model's capability. It reflects Google's Gemma 4 architecture, repackaged by the LM Studio community for local deployment.

Capabilities

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

Creative Writing

Moderate

Multilingual

Moderate

Multimodal

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.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.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.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.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.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.