MiniMax-M2.5

About

What MiniMax-M2.5 is

Model Overview Description: The NVIDIA MiniMax-M2.5-NVFP4 model is the quantized version of MiniMax's MiniMax-M2.5 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA MiniMax-M2.5 NVFP4 model is quantized with Nvidia Model Optimizer. This model is ready for commercial/non-commercial use. <br Third-Party Community Consideration This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (MiniMax-M2.5) Model Card. License/Terms of Use: Governing Terms: Use of this model is governed by the NVIDIA Open Model License . ADDITIONAL INFORMATION : Modified MIT License. MiniMax M2.5 . Deployment Geography: Global <br Use Case: Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. <br Release Date: Huggingface 03/25/2026 via https://huggingface.co/nvidia/MiniMax-M2.5-NVFP4 <br Model Architecture: Architecture Type: Transformers <br Network Architecture: MiniMaxM2ForCausalLM <br This model was developed based on MiniMax-M2.5 <br Number of model parameters: 229B <br Input: Input Type(s): Text <br Input Format(s): String <br Input Parameters: 1D (One-Dimensional): Sequences <br Other Properties Related to Input: Context length 196,608 tokens <br Output: Output Type(s): Text <br Output Format: String <br Output Parameters: 1D (One-Dimensional): Sequences <br Other Properties Related to Output: None <br Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br Software Integration: Runtime Engine(s): <br SGLang <br vLLM <br Supported Hardware Microarchitecture Compatibility: <br NVIDIA Blackwell <br Preferred Operating System(s): <br Linux <br The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys minimax-m2-5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minimax-m2-5 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minimax-m2-5","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Commercial licence needed

Sources

Weights & code

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