Qwen3.8-27B
See Qwen3.8-27B in action: demonstration videos
For improved performance, download and enable a compatible MTP speculative decoder.
<table style="border-collapse: collapse; border: none; text-align:left; margin-top:10px; margin-bottom:0px;">
<tr><td><strong>Text inference</strong></td><td>~19.78 tokens/s @ 1000 tokens ~28.34 GiB (debug build)</td></tr>
<tr><td><strong>Vision inference</strong></td><td>~17.1 tokens/s ~28.36 GiB (debug build)</td></tr>
<tr><td><strong>Vision inference (with MTP)</strong></td><td>~29.04 tokens/s ~29.56 GiB (debug build)</td></tr>
</table>

<p style="margin-bottom:0px;">
<strong>Q9-bit quant typically achieves near lossless accuracy</strong>
</p>
<table style="border-collapse: collapse; border: none; 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>Q3.5</strong></td><td>168.0</td><td>43.45%</td><td>72.57%</td></tr>
<tr><td><strong>Q4.5</strong></td><td>1.33593</td><td>91.65%</td><td>17.28%</td></tr>
<tr><td><strong>Q5.5</strong></td><td>1.23437</td><td>95.05%</td><td>17.28%</td></tr>
<tr><td><strong>Q6.5</strong></td><td>1.21875</td><td>96.65%</td><td>12.03%</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>Q9</strong></td><td>1.20312</td><td>97.80%</td><td>9.60%</td></tr>
<tr><td><strong>Base</strong></td><td>1.20312</td><td>100%</td><td>0.000%</td></tr>
</tbody>
</table>
<ul>
<li style="margin:0;">Perplexity: Measures the confidence for predicting base tokens (lower is better)</li>
<li style="margin:0;">Token Accuracy: The percentage of correctly generated base tokens</li>
<li style="margin:0;">Missed Divergence: Measures severity of misses; how much the token was missed by</li>
</ul>
Quantized with a modified version of MLX
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.