How to Install GLM-5-FP8 Windows 11 No-Code Guide

How to Install GLM-5-FP8 Windows 11 No-Code Guide

Running this model locally is fastest when deployed through Docker.

Follow the sequence of steps detailed below.

After that, launch the environment using docker-compose.

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  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: required: 16 GB absolute minimum for small models
  • Disk: 150+ GB for high-context vector database storage
  • 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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