Muse Glimmer 30B NVFP4 W4A4

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

Released August 2026context N/A

Muse Glimmer 30B NVFP4 W4A4 is a 30-billion-parameter multimodal model that accepts both text and image inputs. It runs in an aggressively quantized format — 4-bit weights and activations using NVIDIA's FP4 scheme — which shrinks memory footprint and speeds up inference at the cost of some numerical precision compared to full-precision variants. Details about its specific task strengths or training focus are limited beyond its multimodal input support.

Capabilities

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

Multimodal

Strong

Instruction Following

Moderate

Reasoning & Logic

Use Case Fit

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

Muse Glimmer 30B NVFP4 W4A4

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

Released August 2026context N/A

Muse Glimmer 30B NVFP4 W4A4 is a 30-billion-parameter multimodal model that accepts both text and image inputs. It runs in an aggressively quantized format — 4-bit weights and activations using NVIDIA's FP4 scheme — which shrinks memory footprint and speeds up inference at the cost of some numerical precision compared to full-precision variants. Details about its specific task strengths or training focus are limited beyond its multimodal input support.

Capabilities

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

Multimodal

Strong

Instruction Following

Moderate

Reasoning & Logic

Use Case Fit

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

Glossary

Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.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 InputThe ability to accept and process multiple types of input data simultaneously, such as both images and text in the same request.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.