What are the hardware requirements for MiniMax M2.7 NVFP4 AutoRound?

MiniMax M2.7 NVFP4 AutoRound requires approximately 49.5 GB of VRAM and supports a context window of 200K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MiniMax M2.7 NVFP4 AutoRound and how large is it?

MiniMax M2.7 NVFP4 AutoRound is developed by intel-ai, a model with 228.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate MiniMax M2.7 NVFP4 AutoRound?

MiniMax M2.7 NVFP4 AutoRound is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MiniMax M2.7 NVFP4 AutoRound by intel-ai — VRAM 49.5GB, 200K context

Name: MiniMax M2.7 NVFP4 AutoRound
Author: intel-ai

MiniMax M2.7 NVFP4 AutoRound is an open-source language model by intel-ai. Features: 228.7b LLM, VRAM: 49.5GB, Context: 200K, License: other.

Arxiv:2309.05516 4-bit Auto-round Base model:minimaxai/minimax-m... Base model:quantized:minimaxai... Conversational Custom code Endpoints compatible Minimax m2 Moe Nvfp4 Nvfp4-plus Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/intel-ai/MiniMax-M2.7-NVFP4-AutoRound

MiniMax M2.7 NVFP4 AutoRound Parameters and Internals

LLM Name	MiniMax M2.7 NVFP4 AutoRound
Repository 🤗	https://huggingface.co/intel-ai/MiniMax-M2.7-NVFP4-AutoRound
Base Model(s)	MiniMaxAI/MiniMax-M2.7 MiniMaxAI/MiniMax-M2.7
Model Size	228.7b
Required VRAM	49.5 GB
Updated	2026-11-05
Maintainer	intel-ai
Model Type	minimax_m2
Model Files	1.4 GB: 1-of-125 1.4 GB: 2-of-125 2.6 GB: 3-of-125 1.4 GB: 4-of-125 1.4 GB: 5-of-125 1.4 GB: 6-of-125 1.4 GB: 7-of-125 0.7 GB: 8-of-125 0.7 GB: 9-of-125 1.4 GB: 10-of-125 1.4 GB: 11-of-125 1.4 GB: 12-of-125 1.4 GB: 13-of-125 1.4 GB: 14-of-125 1.4 GB: 15-of-125 1.4 GB: 16-of-125 1.4 GB: 17-of-125 1.4 GB: 18-of-125 0.7 GB: 19-of-125 0.7 GB: 20-of-125 1.4 GB: 21-of-125 1.4 GB: 22-of-125 0.7 GB: 23-of-125 1.4 GB: 24-of-125 1.4 GB: 25-of-125 0.7 GB: 26-of-125 1.4 GB: 27-of-125 1.4 GB: 28-of-125 0.7 GB: 29-of-125 1.4 GB: 30-of-125 0.7 GB: 31-of-125 1.4 GB: 32-of-125 1.4 GB: 33-of-125 1.4 GB: 34-of-125 0.7 GB: 35-of-125 1.4 GB: 36-of-125 0.7 GB: 37-of-125 0.7 GB: 38-of-125 1.4 GB: 39-of-125 1.4 GB: 40-of-125
Model Architecture	MiniMaxM2ForCausalLM
License	other
Context Length	204800
Model Max Length	204800
Transformers Version	4.46.1
Vocabulary Size	200064

Rank the MiniMax M2.7 NVFP4 AutoRound capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to MiniMax M2.7 NVFP4 AutoRound

Best Alternatives	Context / RAM	Downloads	Likes
MiniMax M2.7	200K / 75.2 GB	923544	1235
List 3.0 Ultra Coder Brain	200K / 75.2 GB	22	10
MiniMax M2.7 BF16	200K / 147.3 GB	18	0
MiniMax M2	192K / 77.7 GB	103020	1492
MiniMax M2.5	192K / 74 GB	772099	1502
MiniMax M2.7	192K / 213 GB	247	2
MiniMax M2.7	192K / 75.2 GB	58	18
MiniMax M2.5	192K / 75.2 GB	19	5
MiniMax M2.1	192K / 75.2 GB	54	13
MiniMax M2.1	192K / 77.7 GB	12798	1358

Note: green Score (e.g. "73.2") means that the model is better than intel-ai/MiniMax-M2.7-NVFP4-AutoRound.