A comparison of the GLM-5.2 and Qwen3.6 large language models on a 64GB Mac revealed that GLM-5.2 is significantly slower, contrary to some claims. The primary reasons are that GLM-5.2, an open-source 753B parameter Mixture of Experts (MoE) model, requires over 217GB even with extreme quantization, far exceeding the Mac's RAM. Furthermore, MoE model speed is determined by active parameters, not total, and GLM-5.2's active 40B parameters are about 13 times more than Qwen3.6's active 3B parameters, leading to a theoretical 13x speed deficit. While a specialized streaming implementation for GLM-5.2 exists, it is experimental, limited to short contexts, and achieves only around 2 tokens/second, making it impractical for the tested hardware. AI
IMPACT Highlights practical limitations of running large MoE models locally and the importance of active parameters over total parameters for speed.
RANK_REASON The article provides a detailed technical comparison and benchmark of two LLMs on specific hardware, offering an opinionated analysis of their performance and suitability.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
