GLM-5.3-MLX-Q4i

GLM 5.3

See GLM 5.3 in action: demonstration videos

Tested with a M3 Ultra using Inferencer app

• Text Inference: ~15.8 tokens/s @ 1000 tokens ~428.6 GiB

<p style="margin-bottom:0px;"> <strong>Q4-INF uses the data-agnostic INF method tuned to yield maximum general accuracy within a 512 GiB memory budget.</strong>

Due to system memory and time constraints, the figures shown below are from the conversion of GLM 5.1.

</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</strong></td><td>1.35937</td><td>89.75%</td><td>28.98%</td></tr> <tr><td><strong>Q4-INF</strong></td><td>1.21093</td><td>97.70%</td><td>10.65%</td></tr> <tr><td><strong>Q5</strong></td><td>1.24218</td><td>94.60%</td><td>17.55%</td></tr> <tr><td><strong>Q6</strong></td><td>1.21875</td><td>96.85%</td><td>16.03%</td></tr> <tr><td><strong>Q8</strong></td><td>1.21875</td><td>97.65%</td><td>9.92%</td></tr> <tr><td><strong>Base</strong></td><td>1.20312</td><td>100.0%</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.

For more details see our demonstration videos or visit zai-org/GLM-5.3.

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