GLM-5.2: How to Run the 753B MoE Model Locally with Unsloth Quantization

GLM-5.2 is a 753-billion-parameter Mixture-of-Experts model that activates only 40 billion parameters per token, making its compute cost comparable to a 40B model while requiring up to 1.51 TB of RAM at full BF16 precision. Running it locally at full precision is impractical for most users, as even eight H100 GPUs provide only around 640 GB of memory. Unsloth's dynamic GGUF quantizations on Hugging Face address this by allocating more bits to critical layers and fewer to less important ones, preserving quality better than traditional uniform quantization. The UD-Q4_K_XL variant at 467 GB is considered the quality sweet spot, while UD-Q3_K_XL at 343 GB is the recommended minimum for users with four GPUs. For general-purpose tasks not involving code or complex reasoning, Unsloth itself suggests the UD-IQ2_M at 239 GB, which retains 82% Top-1 accuracy relative to the full BF16 model.