---
title: sarvamai-30b-MLX-Q10
canonical_url: "https://www.modelscope.ai/models/inferencerlabs/sarvamai-30b-MLX-Q10"
md_url: "https://www.modelscope.ai/models/inferencerlabs/sarvamai-30b-MLX-Q10.md"
repository: inferencerlabs/sarvamai-30b-MLX-Q10
last_updated: 2026-06-20
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - sarvam_moe
architectures:
  - SarvamMoEForCausalLM
base_model:
  - sarvamai/sarvam-30b
base_model_relation: quantized
parameters: 10.8B
tensor_type:
  - F32
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 227
stars: 0
tags:
  - quantized
  - mlx
---

# sarvamai-30b-MLX-Q10

> sarvamai-30b-MLX-Q10 - An open-source model by inferencerlabs on ModelScope. See sarvam-30b MLX in action - demonstration video

inferencerlabs/sarvamai-30b-MLX-Q10 is a 10.8B-parameter text-generation model on ModelScope. derived from sarvamai/sarvam-30b.

- **Repository**: inferencerlabs/sarvamai-30b-MLX-Q10
- **Tasks**: text-generation
- **Parameters**: 10.8B
- **Base model**: sarvamai/sarvam-30b
- **Tags**: quantized, mlx
- **Downloads**: 227
- **Stars**: 0
- **Last updated**: 2026-06-20

Source: https://www.modelscope.ai/models/inferencerlabs/sarvamai-30b-MLX-Q10

---

**See sarvam-30b MLX in action - [demonstration video](https://youtu.be/e3L_xwvjEM0)**

#### Tested on a M3 Ultra 512GB RAM using [Inferencer app](https://inferencer.com)
- Single inference ~44 tokens/s @ 1000 tokens (measured in debug mode)
- Batched inference ~ total tokens/s across five inferences
- Memory usage: ~42.2 GiB

<p style="margin-bottom:0px;">
<strong>10bpw quant typically achieves near lossless accuracy in our coding test</strong>
</p>
<table style="border-collapse: collapse; text-align:center; margin-top:10px; margin-bottom:0px;">
<thead>
<tr><th>Quantization (bpw)</th><th>Perplexity</th><th>Token Accuracy</th><th>Missed Divergence</th></tr>
</thead>
<tbody>
<tr><td><strong>q4.5</strong></td><td>1.32812</td><td>90.5%</td><td>26.44%</td></tr>
<tr><td><strong>q5.5</strong></td><td>1.23437</td><td>95.4%</td><td>16.03%</td></tr>
<tr><td><strong>q6.5</strong></td><td>1.21875</td><td>96.85%</td><td>12.55%</td></tr>
<tr><td><strong>q8.5</strong></td><td>1.21875</td><td>97.65%</td><td>9.92%</td></tr>
<tr><td><strong>q10</strong></td><td>1.21093</td><td>97.95%</td><td>9.61%</td></tr>
<tr><td><strong>Base</strong></td><td>1.20312</td><td>100.0%</td><td>0.000%</td></tr>
</tbody>
</table>

##### Quantized with a modified version of [MLX](https://github.com/ml-explore/mlx)
##### For more details see [demonstration video](https://youtu.be/e3L_xwvjEM0) or visit [sarvam-30b](https://huggingface.co/sarvamai/sarvam-105b).

## 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.