---
license: other
base_model: MiniMaxAI/MiniMax-M2.5
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - minimax
---

# MiniMax-M2.5 APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).

**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)

## Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see [mudler/Qwen3.5-35B-A3B-APEX-GGUF](https://huggingface.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF).

## Available Files

| File | Profile | Size | Best For |
|------|---------|------|----------|
| MiniMax-M2.5-APEX-I-Balanced.gguf | I-Balanced | 155 GB | Best overall quality/size ratio |
| MiniMax-M2.5-APEX-I-Quality.gguf | I-Quality | 130 GB | Highest quality with imatrix |
| MiniMax-M2.5-APEX-Quality.gguf | Quality | 130 GB | Highest quality standard |
| MiniMax-M2.5-APEX-Balanced.gguf | Balanced | 155 GB | General purpose |
| MiniMax-M2.5-APEX-I-Compact.gguf | I-Compact | 100 GB | Multi-GPU setups, best quality/size |
| MiniMax-M2.5-APEX-Compact.gguf | Compact | 100 GB | Multi-GPU setups |
| MiniMax-M2.5-APEX-I-Mini.gguf | I-Mini | 81 GB | Smallest viable |

## 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).

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

## Architecture

- **Model**: MiniMax-M2.5 (MiniMaxM2)
- **Layers**: 62
- **Experts**: 256 routed + 1 shared (8 active per token)
- **Total Parameters**: 228.7B
- **Active Parameters**: ~45B per token
- **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-M2.5-APEX-GGUF@MiniMax-M2.5-APEX-I-Balanced.gguf
```

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).