GLM-5.3-SLIM-GGUF reduces a 753-billion-parameter mixture-of-experts model to 563 billion parameters, allowing the quantized weights to run on a single NVIDIA B200. A neural architecture search selected 192 of the 256 routed experts in each MoE layer, and the conversion removed the remaining 64 without retraining or recalibration.

The release combines Unsloth’s UD-IQ2_M quantization of GLM-5.3 with the expert selection from GLM-5.3-SLIM-E192. GGUF is a model format designed for runtimes such as llama.cpp; here, its tensor layout makes it possible to delete complete experts while preserving the selected quantized weights byte for byte.

A 58.7 GB cut

The slim release averages about 2.56 bits per weight. Its 238.6 GB parent exceeds the memory available on one B200 and a 192 GB Mac, forcing multi-device inference. On the tested B200, the authors report that the pruned model remains entirely on the GPU while leaving enough memory for roughly 100,000 tokens of shared key-value cache across as many as 10 concurrent requests.

Scores without a matched baseline

A matched run of the 256-expert parent is required to quantify the quality lost through pruning and quantization. The harness also differs from the official GLM-5.3 evaluation, so these figures should be interpreted within the published setup rather than compared directly with the model card.