🧠MiniMax‑M1: Lightning‑Fast Open Source faster than CHAT GPT
⚙️ Introduction to MiniMax‑M1
MiniMax‑M1 is a groundbreaking open‑weight large‑scale reasoning model featuring a hybrid Mixture‑of‑Experts (MoE) architecture paired with ultra‑efficient lightning attention. Evolving from MiniMax‑Text‑01 (456 B params), M1 activates 45.9 B params per token and natively handles 1 million‑token context windows—8× larger than DeepSeek R1. Its lightning attention achieves 75% FLOP savings at 100k-generation length compared to DeepSeek R1.
Trained with a large‑scale reinforcement learning (RL) framework across mathematics, software engineering, and sandbox environments, M1 introduces CISPO, a novel RL algorithm that clips importance‑sampling weights for superior stability. Two model variants support 40k and 80k token “thinking budgets.” On complex reasoning, coding, and long‑context benchmarks, MiniMax‑M1 surpasses DeepSeek‑R1 and Qwen3‑235B, establishing a powerful new foundation for reasoning AI agents.
🧠MiniMax‑M1 Overview try from here
MiniMax‑M1 is the world’s first open-weight, large-scale hybrid-attention reasoning model, featuring:
- A hybrid Mixture-of-Experts (MoE) architecture:
- Total size: 456 billion parameters
- Active per token: 45.9 billion
- Sparse expert selection via top‑k gating
- Support for 1 million token context windows—8× larger than DeepSeek R1
- Lightning attention mechanism:
- Combines sparse (lightning) blocks with occasional softmax blocks
- Enables linear-time attention with build-in scalability
- Consumes only 25% of FLOPs vs. DeepSeek R1 at 100K tokens
🚀 RL Training with CISPO
- Trained via large-scale reinforcement learning across domains like math, software engineering, and sandbox environments
- Introduces CISPO (Clipped Importance Sampling Policy Optimization):
- Clips importance-sampling weights (not gradients)
- Boosts training stability and efficiency
- Enables RL training in just 3 weeks on 512 H800 GPUs for ~$535K
🔬 Model Variants & Strengths
- Comes in two versions:
- MiniMax‑M1‑40K – standard reasoning
- MiniMax‑M1‑80K – extended “thinking budget”
- Outperforms DeepSeek‑R1 and Qwen3‑235B on benchmarks involving:
- Long-context reasoning
- Code and software engineering tasks
- Tool use
⚖️ Why It Matters
MiniMax‑M1 uniquely combines scale, efficiency, and openness:
- Only 45.9B active parameters per token, enabling computation-light inference
- Lightning attention + MoE achieves linear scaling in attention operations
- 1 million token context supports deep reasoning across entire books, large codebases, or long conversations
- Full transparency under Apache 2.0, empowering researchers and developers
🧩 Architecture Diagram
A visual overview of MiniMax‑M1’s hybrid MoE + Lightning Attention architecture, illustrating how sparse MoE blocks and efficient attention layers interoperate.()
đź“Š Benchmark Table
| Task | MiniMax‑M1‑80K | MiniMax‑M1‑40K | DeepSeek‑R1 | Qwen3‑235B |
|---|---|---|---|---|
| AIME 2024 (Math) | 86.0 % | 83.3 % | 79.8 % | 85.7 % |
| LiveCodeBench (Coding) | 65.0 % | 62.3 % | 55.9 % | 65.9 % |
| SWE‑bench Verified (SW Eng.) | 56.0 % | 55.6 % | 49.2 % | 34.4 % |
| TAU‑bench (Tool Use) | 62.0 % | 60.0 % | 53.5 % | 34.7 % |
| OpenAI‑MRCR (128k Context) | 73.4 % | 76.1 % | 51.5 % | 27.7 % |
| OpenAI‑MRCR (1M Context) | 56.2 % | 58.6 % | N/A | N/A |
⚙️ Integration Examples
- GitHub Repository:
Official code, model weights, and tech report under Apache‑2.0 license. Come exploremodeling_minimax_m1.py, inference scripts, and integration examples. - Hugging Face Model Hub:
Includes both 40K and 80K variants, with deployment options via Transformers, vLLM, and API access. Installation examples: pythonCopyEditpip install transformers vllm from transformers import pipeline pipeline('text-generation', model='MiniMax-AI/MiniMax-M1-80k') ```([turn0search2](#cited)) - Production Deployment:
Supports vLLM for optimized, high-throughput serving; recommended for latency-sensitive applications.
đź’ˇ Visual Insights
- Benchmark bar charts show MiniMax‑M1‑80K consistently outperforms leading open-weight counterparts across mathematics, software engineering, reasoning, tool use, and long-context tasks—even rivaling some closed-weight models
- FLOPs vs. sequence length graph indicates ~25% compute cost compared to DeepSeek‑R1 at 100K tokens, reflecting lightning attention efficiency.
🖥️ Minimum Hardware & Software Specifications
âś… Hardware Requirements
| Component | Minimum | Recommended |
|---|---|---|
| GPU | NVIDIA RTX 4090 / A6000 (24 GB VRAM) minimax01.com+15atalupadhyay.wordpress.com+15onedollarvps.com+15 | 8× NVIDIA H800 / H20 GPUs (~350 GB VRAM) |
| System RAM | 64 GB | — |
| Storage | 1 TB SSD | — |
| CPU | 8+ cores | — |
| Network | High-speed internet | — |
⚙️ Software Requirements
- OS: Linux (Ubuntu 20.04+) or macOS; Docker support for Windows environments
- Python: 3.10+
- CUDA: 11.8+; for vLLM usage CUDA versions supported accordingly
- Libraries: PyTorch ≥2.0.0, transformers, accelerate, vllm, datasets, numpy, pandas, matplotlib, seaborn, tqdm
- Optional: wandb, gradio, langchain, sentence-transformers, faiss-cpu
🚀 Installation Guide
Step 1: Clone & Setup Environment
conda create -n minimax-m1 python=3.10
conda activate minimax-m1
git clone https://github.com/MiniMax-AI/MiniMax-M1
cd MiniMax-M1
Step 2: Install Dependencies
pip install torch>=2.0.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers>=4.36 accelerate>=0.24 vllm>=0.2 datasets>=2.14 numpy>=1.24 pandas>=2.0 matplotlib>=3.7 seaborn>=0.12 tqdm>=4.65
# optional tools:
pip install wandb gradio langchain sentence-transformers faiss-cpu
Step 3: Download Model Weights
pip install -U huggingface-hub
huggingface-cli download MiniMaxAI/MiniMax-M1-40k
# or MiniMax-M1-80k
Ensure git lfs is installed to fetch full weight files .
Step 4: Deploy with vLLM (Recommended)
Docker Deployment:
docker pull vllm/vllm-openai:v0.8.3
docker run -it \
-v $MODEL_DIR:$MODEL_DIR \
-v $CODE_DIR:$CODE_DIR \
--network=host --privileged --ipc=host --shm-size=2g \
--gpus all \
vllm/vllm-openai:v0.8.3 /bin/bash
Inside container:
export SAFETENSORS_FAST_GPU=1
export VLLM_USE_V1=0
python3 -m vllm.entrypoints.openai.api_server \
--model $MODEL_DIR \
--tensor-parallel-size 8 \
--trust-remote-code \
--quantization experts_int8 \
--max_model_len 4096
Direct vLLM Install:
pip install vllm
📡 Integration & Usage
🔹 Hugging Face Transformers Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M1-80k", torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)
🔹 vLLM API Usage
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"MiniMaxAI/MiniMax-M1-80k", "messages":[{"role":"user","content":[{"type":"text","text":"Your prompt here"}]}]}'
đź“Ś Summary
- Hardware: 24 GB GPU VRAM minimum; 8× H800/H20 GPUs for full performance
- Software: Python 3.10+, PyTorch, CUDA 11.8+, vLLM, Transformers
- Deployment: vLLM via Docker (recommended) or direct install; Transformers support for experimentation
- Model Access: Download weights from Hugging Face with
git lfs
🛠️ Quantization & Low-Resource Setups
To deploy MiniMax‑M1 efficiently on constrained hardware, consider:
- 8-bit (INT8) Quantization:
- Maintains near-original performance while reducing memory by ~4Ă— reddit.com+15reddit.com+15arxiv.org+15.
- Supported in the Transformers ecosystem via QuantoConfig for weight-only quantization github.com+1arxiv.org+1.
- 4-bit (INT4) Quantization & Emerging Mixed-Precision:
- Offers higher compression but may introduce moderate accuracy drops .
- Techniques like SageAttention2 (INT4) and AMXFP4 maintain quality with quantization-aware approaches analyticsvidhya.com+13blog.openvino.ai+13reddit.com+13.
- Low-Resource Strategy:
- Run the 40K variant locally with 8–24 GB GPU RAM after quantization.
- Match quantization level (e.g., Q4 to Q8) to available GPU VRAM for best performance‑vs‑quality balance github.com+1reddit.com+1reddit.com+2reddit.com+2reddit.com+2.
đź”® Future Work
MiniMax‑M1 sets the stage for further advancements:
- Ultra-Low-Bit Quantization:
- Explore 3-bit quantization (e.g. via SqueezeLLM) to enable deployment in extremely limited environments github.com+13labellerr.com+13ubos.tech+13analyticsvidhya.com+9reddit.com+9arxiv.org+9.
- Dynamic Mixed-Precision Models:
- Utilize nested “Matryoshka” quantization for dynamic switching between precision levels arxiv.org+9reddit.com+9reddit.com+9.
- Efficiency Across Platforms:
- Investigate CPU/mobile deployment using micro-scaling FP quant formats like AMXFP4 and SageAttention2 arxiv.org+7reddit.com+7pmc.ncbi.nlm.nih.gov+7labellerr.com+4blog.openvino.ai+4reddit.com+4.
- RL & Attention Innovations:
- Extend CISPO to adaptively adjust RL strategies during fine-tuning and optimize lightning attention scaling under varying loads arxiv.org+4ubos.tech+4minimax-m1.blog+4arxiv.org+8arxiv.org+8huggingface.co+8.
- Multimodal & Agent Integration:
- Merge with MiniMax’s VL and agent frameworks for video, image, and tool-augmented reasoning pipelines.
âś… Conclusion
MiniMax‑M1 stands as a landmark in open-weight reasoning models, delivering:
- Hybrid MoE + Lightning Attention: Efficient scaling to 1M token context, with ~75% compute savings analyticsvidhya.com+12arxiv.org+12bestaiagents.today+12.
- Scalable RL via CISPO: Robust training on 512 H800 GPUs in just three weeks analyticsvidhya.com+4arxiv.org+4labellerr.com+4.
- Flexible Deployment: Through quantization and vLLM support, it can be tailored for both high-end servers and moderate local GPU setups.
Moving forward, advancements in quantization, modular deployment, and extended multimodal capabilities will further strengthen MiniMax’s position as a foundational model for the next AI generation.
đź“š References
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention, arXiv, June 2025 labellerr.com+8arxiv.org+8en.wikipedia.org+8
- MiniMax‑01: Scaling Foundation Models with Lightning Attention, arXiv, Jan 2025 arxiv.org+15arxiv.org+15arxiv.org+15
- Low-bit Quantization of Neural Networks for Efficient Inference, arXiv, Feb 2019 reddit.com+4arxiv.org+4blog.openvino.ai+4
- QuantoConfig & Transformers Quantization Guide, GitHub github.com
- Reddit on Quantization Levels & Tips, insights on INT4/INT8 tradeoffs reddit.com+3reddit.com+3reddit.com+3
- SageAttention2 / AMXFP4 for FP4 LLM Inference, OpenVINO Blog, Q1 2023 blog.openvino.ai
