Muse Glimmer 30B 4bit

Muse

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

Released August 2026context N/A

A multimodal model that accepts both text and image inputs, producing text outputs. It operates at 4-bit quantization, which reduces memory footprint at the cost of some precision compared to full-precision variants. As an open-weight release from mlx-community, it fits into workflows where local deployment and accessibility matter.

Muse Glimmer 30B 4bit

Muse

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

Released August 2026context N/A

A multimodal model that accepts both text and image inputs, producing text outputs. It operates at 4-bit quantization, which reduces memory footprint at the cost of some precision compared to full-precision variants. As an open-weight release from mlx-community, it fits into workflows where local deployment and accessibility matter.

Glossary

4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.AccessibilityDesigning technology so people with disabilities can use it effectively.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.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.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.