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Run GLM-4.7-Flash Uncensored Edition

The fastest way to get this model running locally is via Docker.

Review and follow the instructions below.

The loader auto-caches the model archive (several GBs included).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

📡 Hash Check: 42a0ff7409b35bf28b97380c7600679b | 📅 Last Update: 2026-06-25



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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.

Parameter Count 26 B
Context Length 128 k tokens
Inference Speed >200 tokens/s
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