Deploy GLM-5-FP8 on AMD/Nvidia GPU with 1M Context Dummy Proof Guide

Deploy GLM-5-FP8 on AMD/Nvidia GPU with 1M Context Dummy Proof Guide

Deploying this model locally is quickest when done via Docker.

Follow the step-by-step instructions below.

1-click setup: the app automatically fetches the large weight files.

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

🖹 HASH-SUM: 250bbee1c43c025dfa45f0685045986a | 📅 Updated on: 2026-06-27



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: at least 100 GB for multiple local LLM variants
  • 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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