foundationpose
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View details ↗DISCOVER & COMPARE
Explore AI models 1050
fourcastnet1
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View details ↗fourcastnet3
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View details ↗Frame-VAD Multilingual MarbleNet v2.0 is a convolutional neural network for voice activity detection (VAD) that serves as the first step for Speech Recognition and Speaker Diarization. It is a frame-based model that outputs a speech probability for each 20 millisecond frame of the input audio. The model has 91.5K parameters, making it lightweight and efficient for real-time applications. To reduce false positive errors — cases where the model incorrectly detects speech when none is present — the model was trained with white noise and real-word noise perturbations. During training, the volume of audios was also varied. Additionally, the training data includes non-speech audio samples to help the model distinguish between speech and non-speech sounds (such as coughing, laughter, and breathing, etc.) **Key Features** - Lightweight model with only 91.5K parameters - Robust against false positive errors - Outputs speech probability for each 20 ms audio frame - Multilingual support: Chinese, English, French, German, Russian, and Spanish Developers, speech processing engineers, and AI researchers will use it as the first step for other speech processing models.
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View details ↗g1_locomanip_finetune
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View details ↗GCViT
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View details ↗GEAR-CHIP
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View details ↗GEAR-SONIC
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View details ↗GEM-X
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View details ↗Gemma-2b-it-ONNX-INT4
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View details ↗Gemma-4-26B-A4B-NVFP4
NVIDIAGemma 4 26B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance. The NVIDIA Gemma 4 26B IT NVFP4 model is quantized with NVIDIA Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Gemma 4 26B IT Model Card **Use Case:** Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.
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View details ↗Gemma-4-31B-IT-NVFP4
NVIDIAGemma 4 31B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance. The NVIDIA Gemma 4 31B IT NVFP4 model is quantized with NVIDIA Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Gemma 4 31B IT Model Card **Use Case:** Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.
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View details ↗GEN3C-Cosmos-7B
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View details ↗geneformer_V1_10M
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View details ↗geneformer_V2_104M
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗geneformer_V2_316M
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View details ↗geotransolver_drivaerml
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View details ↗gliner-PII
NVIDIAGLiNER-PII is inspired by the Gretel GLiNER PII/PHI models. Built on the GLiNER large-v2.1 base, it detects and classifies a broad range of Personally Identifiable Information (PII) and Protected Health Information (PHI) in structured and unstructured text. It is non-generative and produces span-level entity annotations with confidence scores across 55+ categories. This model was developed by NVIDIA. GLiNER-PII supports detection and redaction of sensitive information across regulated and enterprise scenarios. Note: performance varies by domain, format, and threshold, so validation and human review are recommended for high‑stakes deployments.
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View details ↗GLM-4.7-NVFP4
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View details ↗GLM-5.1-NVFP4
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View details ↗GLM-5.2-NVFP4
NVIDIAThe 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. **GOVERNING TERMS:** Use of the model is governed by the MIT License, same as the base model. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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View details ↗GLM-5.3-Flash-FP4-Local
NVIDIA**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**
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View details ↗GLM-5.3-Flash-NVFP4
NVIDIAThe NVIDIA GLM-5.3-Flash NVFP4 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 NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (GLM-5.3-Flash) Model Card from ZAI. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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