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GLM-5.3-Flash pruned to half the experts for coding: Q3 (71 GB) solves every task the full 341 GB model solves, IQ2_XXS fits 24 GB VRAM + 32 GB RAM

I wanted a coder version from GLM 5.3-Flash. So, I pruned GLM-5.3-Flash (321B MoE) from 288 to 144 routed experts per layer, choosing the experts on agentic coding sessions instead of a generic corpus, then quantized it with an imatrix built on the same sessions. Results. 26 Python SWE tasks from a…

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  1. 2026-10-13 16:04 · r/LocalLLM
    GLM-5.3-Flash pruned to half the experts for coding: Q3 (71 GB) solves every task the full 341 GB model solves, IQ2_XXS fits 24 GB VRAM + 32 GB RAM

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