Model reference · open weights
GLM-4.5-Air is an open-weight language model from zai-org. GLM-4.5-Air-FP8 (FP8) weighs 113 GB; the smallest configuration that runs it is 4× RTX 5090 32 GB.
Summary of the zai-org/GLM-4.5-Air model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | zai-org |
|---|---|
| Released | 2025-07-20 |
| Parameters | 110.5B |
| VRAM | 113 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … DGX Spark (GB10) 128 GB unified 10 smaller cards | — | — | — | |
| H200 141 GB | 10 | 2 | 86K | 138 GB |
| B200 180 GB | 32 | 8 | all 128K | 176 GB |
| 4× RTX 5090 32 GB tensor parallel | 3 | — | 25K | 31.0 GB a card |
| 2× H100 80 GB tensor parallel | 16 | 4 | all 128K | 78.1 GB a card |
| 2× A100 80 GB tensor parallel · FP8 without its speed-up here | 26 | 6 | all 128K | 78.2 GB a card |
| 4× L40S 48 GB tensor parallel | 36 | 9 | all 128K | 44.0 GB a card |
| 2× RTX PRO 6000 Blackwell 96 GB tensor parallel | 37 | 9 | all 128K | 93.8 GB a card |
| 2× H200 141 GB tensor parallel | 94 | 23 | all 128K | 138 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 116 GB | 120 GB |
| 5 | 122 GB | 145 GB |
| 8 | 127 GB | 164 GB |
| 16 | 139 GB | 213 GB |
| 32 | 164 GB | 312 GB |
| 64 | 213 GB | 509 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| GLM-4.5-Air ↗ | 110.5B | BF16 | 221 GB | 2× H200 141 GB |
| GLM-4.5-Air-FP8 (above) ↗ | 110.5B | FP8 | 113 GB | 4× RTX 5090 32 GB |
From the model card
📖 Check out the GLM-4.5 technical blog, technical report, and Zhipu AI technical documentation. 📍 Use GLM-4.5 API services on Z.ai API Platform (Global) or Zhipu AI Open Platform (Mainland China). 👉 One click to GLM-4.5.
The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.
As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of 63.2, in the 3rd place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at 59.8 while maintaining superior efficiency.
For more eval results, show cases, and technical details, please visit our technical blog or technical report.
The model code, tool parser and reasoning parser can be found in the implementation of transformers, vLLM and SGLang.
Please refer our github page for more detail.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.