---
title: MiniMax-M2.5-MLX-Q9-archive
canonical_url: "https://www.modelscope.ai/models/inferencerlabs/MiniMax-M2.5-MLX-Q9-archive"
md_url: "https://www.modelscope.ai/models/inferencerlabs/MiniMax-M2.5-MLX-Q9-archive.md"
repository: inferencerlabs/MiniMax-M2.5-MLX-Q9-archive
last_updated: 2026-06-20
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - minimax_m2
architectures:
  - MiniMaxM2ForCausalLM
base_model:
  - MiniMaxAI/MiniMax-M2.5
base_model_relation: quantized
parameters: 71.5B
tensor_type:
  - BF16
  - F32
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 84
stars: 0
tags:
  - quantized
  - mlx
---

# MiniMax-M2.5-MLX-Q9-archive

> MiniMax-M2.5-MLX-Q9-archive - An open-source model by inferencerlabs on ModelScope. See MiniMax-M2.5 MLX in action - demonstration video

inferencerlabs/MiniMax-M2.5-MLX-Q9-archive is a 71.5B-parameter text-generation model on ModelScope. derived from MiniMaxAI/MiniMax-M2.5.

- **Repository**: inferencerlabs/MiniMax-M2.5-MLX-Q9-archive
- **Tasks**: text-generation
- **Parameters**: 71.5B
- **Base model**: MiniMaxAI/MiniMax-M2.5
- **Tags**: quantized, mlx
- **Downloads**: 84
- **Stars**: 0
- **Last updated**: 2026-06-20

Source: https://www.modelscope.ai/models/inferencerlabs/MiniMax-M2.5-MLX-Q9-archive

---

**See MiniMax-M2.5 MLX in action - [demonstration video](https://youtu.be/yWXK6zu_kGE)**

#### Tested on a M3 Ultra 512GB RAM using [Inferencer app v1.10](https://inferencer.com)
- Single inference ~36.5 tokens/s @ 1000 tokens
- Batched inference ~44 total tokens/s across two inferences
- Memory usage: ~239 GiB

*q9bit quant typically achieves near lossless accuracy in our coding test*
| Quantization | Perplexity | Token Accuracy | Missed Divergence |
|:------------:|:----------:|:--------------:|:-----------------:|
| **q3.5**     | 168.0      | 43.45%         | 72.57%            |
| **q4.5**     | 1.33593    | 91.65%         | 27.61%            |
| **q4.8**     | 1.28125    | 93.75%         | 21.15%            |
| **q5.5**     | 1.23437    | 95.05%         | 17.28%            |
| **q6.5**     | 1.21875    | 96.95%         | 12.03%            |
| **q8.5**     | 1.21093    | 97.55%         | 10.50%            |
| **q9**       | 1.21093    | 97.55%         | 10.50%            |
| **Base**     | 1.20312    | 100.0%         | 0.000%            |

- Perplexity: Measures the confidence for predicting base tokens (lower is better)
- Token Accuracy: The percentage of correctly generated base tokens
- Missed Divergence: Measures severity of misses; how much the token was missed by

##### Quantized with a modified version of [MLX](https://github.com/ml-explore/mlx)
##### For more details see [demonstration video](https://youtu.be/yWXK6zu_kGE) or visit [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).

## Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.