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Z.ai releases GLM-5.3-Flash: a 320B multimodal MoE with 18B active, MIT licence

Z.ai released GLM-5.3-Flash on August 26, the first natively multimodal model in its GLM-5 series, with open weights on Hugging Face under the MIT licence. It has 320B parameters in total with 18B active per token and, for the first time in a GLM model, combines sparse and linear attention to cut the cost of long contexts. The model card lists vLLM, SGLang, KTransformers and Unsloth among the ways to run it. The bigger GLM-5.3, built on the GLM-5.2 base and improved through post-training, also has open weights, under a separate GLM-5.3 licence.

Why it matters locally

Even at 4 bits per parameter the weights alone take more than 160 GB, so this is a model for 256 GB-class Macs or multi-GPU servers; llama.cpp added support in v0.6.0 on October 5.

Source

Read the official source: GLM-5.3-Flash model card (Hugging Face) ↗

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