# GLM-5.2-NVFP4 by NVIDIA: Open-Weight Model
Source: https://savrn.com/models/glm-5-2-nvfp4
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Runs On

What it takes to serve GLM-5.2-NVFP4 (381B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 762.0 GB | 914.4 GB | 4x [MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) (256 GB) Vultr | $8.00 | [5x MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) $9.25 · [4x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $10.36 |
| 8-bit | 381.0 GB | 457.2 GB | 2x [MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) (256 GB) Vultr | $4.00 | [2x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $5.18 · [3x MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) $5.55 |
| 4-bit | 190.5 GB | 228.6 GB | 1x [MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) (256 GB) Vultr | $2.00 | [1x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $2.59 · [2x MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) $3.70 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026.

[GLM-5.2-NVFP4 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/glm-5-2-nvfp4/gpus)

## SAVRN's Notes on GLM-5.2-NVFP4

381B parameters, 256 experts with 8 active per token, a 1,048,576-token context: this is a frontier-scale mixture-of-experts, and the Runs On table prices it accordingly. At 16-bit you need 914.4 GB — four MI325X cards at $8.00 an hour. At 8-bit, two MI325X at $4.00. At 4-bit, one MI325X at $2.00. The 464.8 GB download arrives as 47 shards of roughly 10 GB each, so budget disk and staging time before you budget GPU.

The license is MIT, unusually clean for this class. The name says NVFP4 and the library column says Model Optimizer — this is NVIDIA's quantized repack of zai-org/GLM-5.2 — while our generic precision table reports the standard 16/8/4-bit lines above, not a measured NVFP4 figure. Treat the 4-bit row as the planning number and the publisher's own calibration as the ceiling. Neighbors on the hub at this scale: K2-Horizon-375B and Qwen3-Coder-480B-FP8.

SAVRN Research

## Model Card

By NVIDIA, published under mit, revision 53e0691e2189.

### 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](https://savrn.com/models/glm-5-2). The NVIDIA GLM-5.2 NVFP4 model is quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer).

This model is ready for commercial or non-commercial use.

#### License/Terms of Use:

GOVERNING TERMS: Use of the model is governed by the [MIT License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md), same as the base model.

#### Deployment Geography:

Global

#### Use Case:

Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

#### Release Date:

Hugging Face 06/25/2026 via https://huggingface.co/nvidia/GLM-5.2-NVFP4

### References

Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer

### Model Architecture:

Architecture Type: Transformers Network Architecture: GLM-5.2 (GlmMoeDsaForCausalLM) Number of Model Parameters: 753B in total and 40B activated

### Input:

[Read the full model card (1,412 words)](https://savrn.com/models/glm-5-2-nvfp4/card)

## Configuration

Architecture

GlmMoeDsaForCausalLM

Context length (tokens)

1,048,576

Layers

78

Hidden size

6,144

Feed-forward size

12,288

Attention heads

64

Key/value heads

64

Head dimension

192

Vocabulary size

154,880

Routed experts

256

Experts

256

Experts active per token

8

Model type

glm_moe_dsa

Quantization

modelopt

## Identity and Version

Repository

nvidia/GLM-5.2-NVFP4

Publisher

NVIDIA

Task

Text generation

Modality

Text

Library

Model Optimizer

Parameters

381B parameters

Languages

Not stated by the source

Revision

53e0691e21895a3863a606dfd12910c69eba94ab

First published

2026-06-22

Last updated

2026-08-31

## Files and Weights

56 files, 464.9 GB in total. The weights are 47 files totalling 464.8 GB in safetensors.

Weights47 files · 464.8 GB

Configuration4 files · 22.2 MB

Tokenizer2 files · 20.2 MB

Documentation1 file · 12.4 KB

Other1 file · 5.1 KB

Repository1 file · 1.6 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model-00001-of-00047.safetensors | Weights | 10.0 GB | 7355ca8065da |
| model-00002-of-00047.safetensors | Weights | 10.0 GB | 5a93bf6840c9 |
| model-00003-of-00047.safetensors | Weights | 10.0 GB | 240485288e04 |
| model-00004-of-00047.safetensors | Weights | 10.0 GB | 790fe046fb8b |
| model-00005-of-00047.safetensors | Weights | 10.0 GB | 9c6a3381b16f |
| model-00006-of-00047.safetensors | Weights | 10.0 GB | 902e254158f0 |
| model-00007-of-00047.safetensors | Weights | 10.0 GB | 40299690b696 |
| model-00008-of-00047.safetensors | Weights | 10.0 GB | 139f1c107077 |
| model-00009-of-00047.safetensors | Weights | 10.0 GB | e1db5be0dac7 |
| model-00010-of-00047.safetensors | Weights | 10.0 GB | 617a2ca6fd37 |
| model-00011-of-00047.safetensors | Weights | 10.0 GB | 5099883c548f |
| model-00012-of-00047.safetensors | Weights | 10.0 GB | df6a0c6ab9fa |
| model-00013-of-00047.safetensors | Weights | 10.0 GB | 2bfd1392eda1 |
| model-00014-of-00047.safetensors | Weights | 10.0 GB | 6320d889ab91 |
| model-00015-of-00047.safetensors | Weights | 10.0 GB | 499d121db3e7 |
| model-00016-of-00047.safetensors | Weights | 10.0 GB | b0bfeb109e4a |
| model-00017-of-00047.safetensors | Weights | 10.0 GB | ca88817f6f02 |
| model-00018-of-00047.safetensors | Weights | 10.0 GB | 4d32422c750f |
| model-00019-of-00047.safetensors | Weights | 10.0 GB | 74ecfce9cddd |
| model-00020-of-00047.safetensors | Weights | 10.0 GB | 23a23304160c |
| model-00021-of-00047.safetensors | Weights | 10.0 GB | db14cdfbe3e5 |
| model-00022-of-00047.safetensors | Weights | 10.0 GB | bf5f1c293e95 |
| model-00023-of-00047.safetensors | Weights | 10.0 GB | 2fb70768ccd1 |
| model-00024-of-00047.safetensors | Weights | 10.0 GB | ccf3b2e4ca1e |
| model-00025-of-00047.safetensors | Weights | 10.0 GB | 67f233905c40 |
| model-00026-of-00047.safetensors | Weights | 10.0 GB | daf23e3ccbf4 |
| model-00027-of-00047.safetensors | Weights | 10.0 GB | 1b4c7404684d |
| model-00028-of-00047.safetensors | Weights | 10.0 GB | a895f3e5a815 |
| model-00029-of-00047.safetensors | Weights | 10.0 GB | 5601bf446f5f |
| model-00030-of-00047.safetensors | Weights | 10.0 GB | 355b7b4665a8 |
| model-00031-of-00047.safetensors | Weights | 10.0 GB | 7bc1ca71bffd |
| model-00032-of-00047.safetensors | Weights | 10.0 GB | d11bd3fe6f4c |
| model-00033-of-00047.safetensors | Weights | 10.0 GB | e39c9ff0d42c |
| model-00034-of-00047.safetensors | Weights | 10.0 GB | a81b529612c9 |
| model-00035-of-00047.safetensors | Weights | 10.0 GB | 70d19f0b6180 |
| model-00036-of-00047.safetensors | Weights | 10.0 GB | cab4d531d014 |
| model-00037-of-00047.safetensors | Weights | 10.0 GB | dc213d3550fc |
| model-00038-of-00047.safetensors | Weights | 10.0 GB | 50c4a5522bd9 |
| model-00039-of-00047.safetensors | Weights | 10.0 GB | ca17fd221731 |
| model-00040-of-00047.safetensors | Weights | 10.0 GB | 103a33ce23be |
| model-00041-of-00047.safetensors | Weights | 10.0 GB | 4d398e69fdd5 |
| model-00042-of-00047.safetensors | Weights | 9.9 GB | 573afa976e2f |
| model-00043-of-00047.safetensors | Weights | 10.0 GB | a459ccf072fc |
| model-00044-of-00047.safetensors | Weights | 10.0 GB | 5ef1a45b4d69 |
| model-00045-of-00047.safetensors | Weights | 10.0 GB | 0fa60cd2ffdd |
| model-00046-of-00047.safetensors | Weights | 10.0 GB | 6edd678c7c89 |
| model-00047-of-00047.safetensors | Weights | 5.1 GB | ca2dfdb8f16f |
| config.json | Configuration | 15.5 KB | — |
| generation_config.json | Configuration | 215 B | — |
| hf_quant_config.json | Configuration | 7.4 KB | — |
| model.safetensors.index.json | Configuration | 22.2 MB | 2aa8397b501d |
| README.md | Documentation | 12.4 KB | — |
| chat_template.jinja | Other | 5.1 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 20.2 MB | 19e773648cb4 |
| tokenizer_config.json | Tokenizer | 790 B | — |

## License and Download

License

mit

Access

Open weights, no gate

Download size

464.8 GB

[Download from NVIDIA](https://huggingface.co/nvidia/GLM-5.2-NVFP4)

Released by NVIDIA through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit).

## Built From

- Derived from [zai-org/GLM-5.2](https://savrn.com/models/glm-5-2)
- Quantized from [zai-org/GLM-5.2](https://savrn.com/models/glm-5-2)

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 464.8 GB |
| 16-bit | 762.0 GB |
| 8-bit | 381.0 GB |
| 4-bit | 190.5 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Compare GLM-5.2-NVFP4

- [DeepSeek-V4-Flash-0731 vs GLM-5.2-NVFP4](https://savrn.com/models/compare/deepseek-v4-flash-0731-vs-glm-5-2-nvfp4)

## Questions About GLM-5.2-NVFP4

### How much GPU memory does GLM-5.2-NVFP4 need?

About 914.4 GB at 16-bit and 228.6 GB at 4-bit: the weights (381B parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run GLM-5.2-NVFP4 on?

At 16-bit, 4x MI325X from $8.00 an hour; at 4-bit, 1x MI325X from $2.00 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use GLM-5.2-NVFP4 commercially?

Yes. GLM-5.2-NVFP4 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

### What is GLM-5.2-NVFP4's context length?

1,048,576 tokens, from the maximum position embeddings in its published configuration.

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## NVIDIA

[All models and datasets](https://savrn.com/model-publishers/nvidia)

## Versions

- [53e0691e2189](https://savrn.com/models/glm-5-2-nvfp4/versions/53e0691e2189) · current 2026-09-18

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- [All text generation models](https://savrn.com/models/tasks/text-generation)
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## Source

- Repository metadata, read 2026-10-19.
- [NVIDIA website](https://build.nvidia.com/models)
- [Hugging Face record](https://huggingface.co/nvidia/GLM-5.2-NVFP4)
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