nvidia/GLM-5.3-Flash-FP4-Local
image-text-to-text model by nvidia
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IMPORTANT: the NVFP4 scale factors in this checkpoint are in a new lossless compression format, but upstream vLLM support for this format has not yet landed. You must use one of the containers provided below. The NVIDIA GLM-5.3-Flash-FP4-Local model is the quantized version of ZAI's GLM-5.3-Flash model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.3-Flash is a natively multimodal Mixture-of-Experts (MoE) model for reasoning, coding, and agentic tasks; it uses a hybrid sparse and linear attention architecture with Manifold-Constrained Hyper-Connections (mHC) to support a long context. For more information, please check here. The NVIDIA GLM-5.3-Flash-FP4-Local model is quantized with Model Optimizer using quantization-aware distillation (QAD). This checkpoint is best suited for local AI hardware and uses approximately 166 GiB of vRAM. It has been validated with vLLM on the following hardware: This model is ready for commercial or non-commercial use. This model is not owned…
Read the full model card ↗ · Preview checked 2026-10-31T19:29:24.687Z
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Model overview
nvidia/GLM-5.3-Flash-FP4-Local is a image-text-to-text model repository published by nvidia on Hugging Face. The source reports the Model Optimizer library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-text
- Library
- Model Optimizer
- Recent downloads (30 days)
- 4,243
- Cumulative likes
- 119
- Hugging Face trending score
- 89
- Reported safetensors parameters
- 175,239,203,374
- Architecture
- Glm5NextForConditionalGeneration
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2026-10-08T16:19:57.000Z
- Last modified
- 2026-10-16T20:28:36.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: zai-org/GLM-5.3-Flash
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-24T06:45:27.935Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.