GLM-4.7-Flash Locally via Ollama 2 Step-by-Step

GLM-4.7-Flash Locally via Ollama 2 Step-by-Step

Using Docker is the absolute quickest way to install this model on your local machine.

Follow the step-by-step instructions below.

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

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🧩 Hash sum → 4f1f311137e81a10157b7424798c1bcb — Update date: 2026-06-26



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: high-speed SSD 120 GB to cache model layers
  • 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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