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Glm 5.2 Nvfp4 model by Eigen Ai Labs
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Model

Glm 5.2 Nvfp4

by Eigen Ai Labs eigen-ai-labs/glm-5.2-nvfp4
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 85
Q: Quality 65
Tech Context
432.01 Params
8.192K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Massive 432.01B Params
8k Context
0 Downloads
H100+ ~327GB Est. VRAM
Dense GLMMOEDSAFORCAUSALLM Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID eigen-ai-labs/glm-5.2-nvfp4
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~326.5GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization. [Multi-GPU / Unified Memory Required]

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eigen_ai_labs_glm_5_2_nvfp4,
  author = {Eigen Ai Labs},
  title = {Glm 5.2 Nvfp4 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/eigen-ai-labs/GLM-5.2-NVFP4}},
  note = {Accessed via Free2AITools.}
}
APA Style
Eigen Ai Labs. (2026). Glm 5.2 Nvfp4 [Model]. Free2AITools. https://huggingface.co/eigen-ai-labs/GLM-5.2-NVFP4

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download eigen-ai-labs/glm-5.2-nvfp4
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 0
Recency (R) 85
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Glm 5.2 Nvfp4: Authority (A:0), Popularity (P:0), Recency (R:85), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--eigen-ai-labs--glm-5.2-nvfp4
slug
eigen-ai-labs--glm-5.2-nvfp4
source
huggingface
author
Eigen Ai Labs
license
MIT
tags
transformers, safetensors, glm_moe_dsa, text-generation, conversational, en, zh, base_model:zai-org/glm-5.2, base_model:quantized:zai-org/glm-5.2, license:mit, endpoints_compatible, 8-bit, modelopt, region:us

βš™οΈ Technical Specs

architecture
GlmMoeDsaForCausalLM
params billions
432.01
context length
8,192
pipeline tag
text-generation
vram gb
326.5
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
0

Data indexed from public sources. Updated daily.