Quick Run GLM-4.7-Flash Using Pinokio Windows

To install this model locally in the shortest time, opt for a direct curl execution.

Carefully read and apply the steps described below.

The setup auto-downloads all needed files (several GBs).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔗 SHA sum: 900185b1f81ee252c7952cb428e6cc0e | Updated: 2026-06-23
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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