---
license: other
base_model: MiniMaxAI/MiniMax-M2.7
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - minimax
  - minimax-m2
---




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# MiniMax-M2.7 APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7).

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

> **Note**: MiniMax M2 architecture support in llama.cpp is still maturing. If you encounter inference issues, ensure you're using a recent llama.cpp build and report issues upstream.

## Available Files

| File | Profile | Size | Best For |
|------|---------|------|----------|
| MiniMax-M2.7-APEX-I-Balanced.gguf | I-Balanced | 155 GB | Best overall quality/size ratio |
| MiniMax-M2.7-APEX-Balanced.gguf | Balanced | 155 GB | General purpose |
| MiniMax-M2.7-APEX-I-Quality.gguf | I-Quality | 129 GB | Highest quality with imatrix |
| MiniMax-M2.7-APEX-Quality.gguf | Quality | 129 GB | Highest quality standard |
| MiniMax-M2.7-APEX-I-Compact.gguf | I-Compact | 100 GB | Multi-GPU setups, best quality/size |
| MiniMax-M2.7-APEX-Compact.gguf | Compact | 100 GB | Multi-GPU setups |
| MiniMax-M2.7-APEX-I-Mini.gguf | I-Mini | 80 GB | Smallest "safe" tier |
| MiniMax-M2.7-APEX-I-Nano.gguf | **I-Nano** (new) | 64 GB | Experimental — IQ2_XXS mid-layer experts |
| MiniMax-M2.7-APEX-F16-*.gguf | F16 reference | 426 GB (10 shards) | Full-precision BF16 for imatrix/further research |

## What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention and shared-expert tensors at higher precision.

See the [APEX project](https://github.com/mudler/apex-quant) for full details, technical report, and scripts.

### Nano (new experimental tier)

APEX M2.7 debuts the **Nano** tier, which pushes mid-layer routed experts to **IQ2_XXS (2.06 bpw)**, near-edge to IQ2_S, edges to Q3_K, and keeps shared experts at Q5_K. About 20% smaller than Mini with modest quality cost, viable only on MoE thanks to sparse per-token activation. Requires imatrix.

Benchmarks for Nano are pending. Feedback welcome.

## Architecture

- **Model**: MiniMax-M2.7 (MiniMaxM2)
- **Layers**: 62
- **Experts**: 256 routed (8 active per token)
- **Total Parameters**: ~228 B
- **Active Parameters**: ~10 B per token
- **Source Format**: FP8 (float8_e4m3fn, block-quantized 128×128)
- **Intermediate Format**: BF16 (via unsloth's pre-converted BF16 GGUF)
- **APEX Config**: 5+5 symmetric edge gradient across 62 layers
- **Calibration**: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

## Run with LocalAI

```bash
local-ai run mudler/MiniMax-M2.7-APEX-GGUF@MiniMax-M2.7-APEX-I-Balanced.gguf
```

## Credits

- **Base model**: [MiniMaxAI](https://huggingface.co/MiniMaxAI)
- **BF16 GGUF source**: [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF)
- **APEX quantization**: [LocalAI](https://github.com/mudler/LocalAI) team
- Built on [llama.cpp](https://github.com/ggerganov/llama.cpp)