🔗 SHA sum: 30b085336239262d40355e83d2e0e662 | Updated: 2026-07-21VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup MiniMax-M2.7-NVFP4 is a highly optimized, 4-bit quantized variant of MiniMaxAI's flagship 230-billion parameter sparse …
MiniMax-M2.7-NVFP4 is a highly optimized, 4-bit quantized variant of MiniMaxAI’s flagship 230-billion parameter sparse Mixture-of-Experts (MoE) foundation model, compressed via NVIDIA Model Optimizer using the cutting-edge NVFP4 format. The architecture leverages a blockwise FP8 scaling scheme per 16 elements, dropping the previous Lightning Attention layers in favor of pure, hardware-optimized Grouped-Query Attention (GQA) with 48 query heads and 8 KV heads. This aggressive mathematical alignment allows the massive model to execute on a mere 10B active parameters per token, reducing VRAM demands dramatically down to 70 GB per GPU in Tensor Parallel setups. Tailored for self-evolving agent loops, multi-file code refactoring, and real-world system debugging, it delivers extreme processing throughput over an expansive 196,608-token context window while maintaining an exceptional score on the SWE-Pro engineering benchmark.
Performance Breakdown
- NVFP4 Quantization Layout: A significant reduction in model size and complexity, resulting in faster inference times and lower power consumption.
- Blockwise FP8 Scales via Nvidia Model Optimizer: An efficient scaling scheme that reduces memory requirements by up to 50% while maintaining high accuracy.
- Grouped-Query Attention (GQA): A novel attention mechanism that achieves state-of-the-art results with significantly reduced compute resources.
Hardware and Software Requirements
| Specification | Detail |
|---|---|
| Total / Active Parameters | 230 Billion Total / 10 Billion Active per Token (Sparse MoE) |
| Quantization Layout | NVFP4 (4-bit Weights with Blockwise FP8 Scales via Nvidia Model Optimizer) |
| Context Window | 196,608 tokens (196k natively) |
| Hardware Baseline | Dual NVIDIA RTX PRO 6000 Blackwell (96GB GDDR7) or H100 Tensor Parallel |
| Attention Mechanism | Standard GQA Softmax (48 Query / 8 KV Heads) |
| Primary Execution Engines | vLLM Native Server, SGLang Backend with b12x |
| Core Benchmarks | SWE-Pro: 56.22% / Terminal Bench 2: 57.0% / VIBE-Pro: 55.6% |
Dedicated Support and Refactoring
For customized support, multi-file code refactoring, or real-world system debugging, our team of experts is available to provide tailored solutions for your specific needs.
MiniMax-M2.7-NVFP4 delivers exceptional performance and efficiency in complex NLP tasks, making it an ideal choice for large-scale language models and applications requiring extreme processing throughput over extensive context windows.
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