GLM-5-FP8 Easy Build

GLM-5-FP8 Easy Build

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

Follow the guidelines below to continue.

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

📄 Hash Value: 5cee42c0845f24edb34d046457e34e6b | 📆 Update: 2026-06-27



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

GLM-5-FP8 is a next-generation language model that leverages *FP8* quantization to deliver high performance on modern hardware. It maintains accuracy and speed while significantly reducing memory usage. The model sets new benchmarks in tasks such as MMLU and Commonsense Reasoning, achieving state-of-the-art results. Its refined transformer block incorporates sparse attention mechanisms for efficient processing of long sequences. A concise overview of its technical specifications is provided below.

Parameter Count 176 B
Context Length 8 K tokens
Quantization FP8
Training FLOPs ≈1.5×10^18
Peak Throughput ≈2 T tokens/s on GPU clusters
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