MODEL PROFILE

GLM-5.3-Flash-FP4-Local

NVIDIA · GLM-5.3-Flash-FP4-Local

**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: * **2 x NVIDIA RTX PRO 6000 Blackwell with 1.8M tokens of FP8 KV Cache** * **2 x NVIDIA DGX Spark 128 GB with 2M tokens of FP8 KV Cache**

Visual QAlicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 21:18:38
OVERVIEW

Model overview

**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: * **2 x NVIDIA RTX PRO 6000 Blackwell with 1.8M tokens of FP8 KV Cache** * **2 x NVIDIA DGX Spark 128 GB with 2M tokens of FP8 KV Cache**

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

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