Setup GLM-5-FP8 on AMD/Nvidia GPU with Native FP4

Setup GLM-5-FP8 on AMD/Nvidia GPU with Native FP4

To install this model locally in the shortest time, opt for Docker.

Follow the sequence of steps detailed below.

The system automatically triggers a cloud download for all heavy weights.

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

📊 File Hash: e0bbe2e2f8ad9d1a740082d65f46833d — Last update: 2026-06-26
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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