Technical breakthroughs brought about by the MoE architecture
The Mixture of Experts architecture adopted by GLM-4.5 is its core technological innovation. The architecture reduces the computational consumption by 60-70% compared to traditional dense models by dynamically activating 32 billion parameters (12 billion for GLMAir) instead of all parameters. For specific implementation, the model contains multiple expert sub-networks, and each input token is routed to the most relevant 2-4 experts for processing. This selective activation mechanism significantly improves the reasoning efficiency while maintaining the model capacity.
Real-world deployment tests show that the GLM-4.5-Air version requires only 16GB of GPU memory (12GB after INT4 quantization) to run, saving 40% of video memory compared to a dense model of the same capacity. In long text processing scenarios, its unique context caching technology reduces duplicate computations by 301 TP3T. These features make it the first 100 billion parameter-level multimodal model to run on consumer GPUs such as the RTX3090, significantly lowering the threshold for enterprise deployment.
This answer comes from the articleGLM-4.5: Open Source Multimodal Large Model Supporting Intelligent Reasoning and Code GenerationThe
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