GLM-5.3 Flash GGUF Quantizations Released via Unsloth for Local Execution
GGUF quantizations for Z.ai's new GLM-5.3 Flash model, created using Unsloth, are now available for local execution. This allows users to run the multimodal Mixture-of-Experts model on local hardware. This release enables developers and AI enthusiasts to run a highly capable 320B parameter model (with 18B active parameters) locally without relying on expensive cloud APIs. It lowers the barrier to accessing advanced coding and agentic capabilities on consumer or workstation-grade hardware. The GLM-5.3 Flash model can be run locally using llama.cpp or Unsloth, requiring 102GB of RAM/VRAM for the 1-bit quantized version and 128GB for the 3-bit version. The model is released under the MIT License, making it highly accessible for various use cases.
## BACKGROUND
GLM-5.3 Flash is a Mixture-of-Experts (MoE) model that features 320 billion total parameters but only activates 18 billion parameters per token, optimizing computational efficiency. GGUF is a popular file format designed for fast loading and running quantized LLMs locally, while Unsloth is an open-source platform that simplifies the local training and execution of these models.