MiniMax M3 FP8 dynamic

MiniMax

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

Released June 2026context N/A

A large multimodal model that accepts both text and image inputs, producing text outputs. It runs in FP8 dynamic quantization, meaning it trades a small amount of numerical precision for significantly reduced memory footprint and faster inference — a practical choice when deploying at scale. The open-weight release via EmbeddedLLM makes it accessible for self-hosted setups.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Strong

Long Context

Use Case Fit

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

MiniMax M3 FP8 dynamic

MiniMax

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

Released June 2026context N/A

A large multimodal model that accepts both text and image inputs, producing text outputs. It runs in FP8 dynamic quantization, meaning it trades a small amount of numerical precision for significantly reduced memory footprint and faster inference — a practical choice when deploying at scale. The open-weight release via EmbeddedLLM makes it accessible for self-hosted setups.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Strong

Long Context

Use Case Fit

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

Glossary

Dynamic QuantizationA quantization approach that adjusts precision levels during inference based on the input data, optimizing the balance between speed and accuracy on-the-fly.FP8 Dynamic QuantizationA compression technique that reduces model size and speeds up inference by representing weights and activations using 8-bit floating-point numbers, with dynamic scaling adjusted per batch to maintain accuracy.FP8 Dynamic QuantizationA specific quantization method that uses 8-bit floating-point numbers and adjusts precision dynamically based on the data being processed, balancing speed and accuracy.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 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.Self-HostedRunning a model on your own hardware and infrastructure instead of relying on a company's servers or API.