GLM-5.2

About

What GLM-5.2 is

Model Overview Description: The NVIDIA GLM-5.2 NVFP4 model is the quantized version of ZAI’s GLM-5.2 model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.2 is a Mixture-of-Experts (MoE) model for reasoning and coding that uses sparse attention (with an IndexShare indexer) to support a long context. For more information, please check here. The NVIDIA GLM-5.2 NVFP4 model is quantized with Model Optimizer. This model is ready for commercial or non-commercial use. <br License/Terms of Use: GOVERNING TERMS: Use of the model is governed by the MIT License, same as the base model. Deployment Geography: Global <br Use Case: <br Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. <br Release Date: <br Hugging Face 06/25/2026 via https://huggingface.co/nvidia/GLM-5.2-NVFP4 <br References Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer Model Architecture: Architecture Type: Transformers <br Network Architecture: GLM-5.2 (GlmMoeDsaForCausalLM) <br Number of Model Parameters: 753B in total and 40B activated <br Input: Input Type(s): Text <br Input Format(s): String <br Input Parameters: One-Dimensional (1D) <br Other Properties Related to Input: Context length up to 1M <br Output: Output Type(s): Text <br Output Format: String <br Output Parameters: 1D (One-Dimensional): Sequences <br Other Properties Related to Output: None <br Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br Software Integration: Supported Runtime Engine(s): <br SGLang <br vLLM <br Supported Hardware Microarchitecture Compatibility: <br NVIDIA Blackwell <br Preferred Operating System(s): <br Linux <br The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testi

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys nvidia-glm-5-2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nvidia-glm-5-2 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia-glm-5-2","messages":[{"role":"user","content":"Hello"}]}'

Details

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Open weights

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