GLM-5.3 Large Language Model Released on Hugging Face

Published Aug 28, 07:02 PM UTC

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The GLM-5.3 large language model has been officially released on the Hugging Face platform. The announcement includes detailed hardware requirements for running the model locally. For FP8 precision, it requires 10-12 H100 or 8 H200 GPUs. For 4-bit/NVFP4 quantization (approximately 390-430GB), it can run on a single 512GB Mac Studio or 4 DGX Spark units. For an aggressive 2-bit quantization (approximately 230-250GB), it can run on a single 256GB Mac Studio or 2 DGX Spark units, though with reduced quality and context length. The post provides a link to the model on Hugging Face.

Source report

GLM-5.3 has been officially released on Hugging Face.

Local Deployment Requirements

The following hardware configurations are recommended for running the model locally:

FP8 precision: 10–12× H100 or 8× H200 GPUs
4-bit / NVFP4 precision (approx. 390–430 GB): One 512 GB Mac Studio or 4× DGX Spark
Aggressive 2-bit precision (approx. 230–250 GB): One 256 GB Mac Studio or 2× DGX Spark (with trade-offs in quality and context length)

For more details, visit: https://t.co/yWqHjsQily

Source

kimmonismusNeutral / independent

Original headline: GLM-5.3 is officially live on Hugging Face. What should run it locally: - FP8: 10–12× H100 or 8× H200 - 4-bit/NVFP4, roughly 390–430GB: one 512GB Mac Studio or 4× DGX Spark - Aggressive 2-bit, roughly 230–250GB: one 256GB Mac

Original language: EN

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