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LatestMiniMaxM2.5
MiniMaxText / LLM

MiniMax Releases M2.5 Mixture-of-Experts Model

The Chinese AI company's first open-weight release uses an efficient FP8 data type but comes with a restrictive, non-commercial license.

Feb 12, 2026
NotableOther
MiniMax-M2.7

Chinese AI company MiniMax has released MiniMax-M2.5, its first contribution to the open-weight model ecosystem. The new text-generation model employs a Mixture-of-Experts (MoE) architecture, a design known for computational efficiency by activating only specialized parts of the network for any given task.

The most notable technical feature of M2.5 is its use of FP8 weights. By quantizing the model's parameters to an 8-bit floating-point format, MiniMax has significantly reduced the model's memory footprint and potentially increased inference speed compared to models using standard 16-bit formats. This makes the model more accessible to developers and researchers working with consumer-grade hardware.

While the model is publicly available, its utility is limited by its license. MiniMax has released M2.5 under a custom license that prohibits commercial use, positioning it primarily as a resource for academic research and non-commercial experimentation. The full model card and license details are available on its Hugging Face repository.

MiniMax-M2.5's release signals another major AI lab entering the open-weight field, contributing a powerful and efficient architecture. However, its restrictive license highlights a growing trend of "look but don't touch" releases, which allow for research and community evaluation while keeping commercial applications off-limits.

Sources

  • MiniMaxAI/MiniMax-M2.5

    Hugging Face

    Visit
OlderZhipu AI Releases Open-Source GLM-5 MoE ModelZhipu AI · Text / LLM · 9 months agoNewerKani-TTS-2 Offers New Open-Source Voice Generationnineninesix · Text → Speech · 9 months ago

Get the model

Hugging Face

Specs

Context window197K tokens
Size230.1 GB
PrecisionFP8
ArchitectureMiniMaxM2ForCausalLM
LicenseOTHER
Downloads335.2K
Likes1.3K

Can you run it?

Needs a multi-GPU server — about 140.5 GB at 4-bit.

  • FP8 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    233.2 GB
  • 4-bit

    2×H100 80 GB · 256 GB Mac Studio

    140.5 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM
4 versions — view changelog

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