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nemotron-3-8b-qa-4k
NVIDIA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 ↗The Nemotron 3 Content Safety model is a Large Language Model (LLM) classifier that uses Google’s Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal and multilingual content-safety related datasets. It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular English-only Llama 3.1 Nemoguard 8b Content Safety and the multilingual Llama 3.1 Nemotron Safety Guard 8B v3, that evaluate the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the Nemotron 8B Content Safety Dataset v2. The model supports 12 languages - English, Arabic, German, Spanish, French, Hindi, Japanese, Thai, Dutch, Italian, Korean and Chinese. The model was trained as a LoRA adapter and the weights were merged back into the parent Gemma-3-4b-it model. Use of this model is governed by the NVIDIA Nemotron Open Model License, Gemma Terms of Use and Gemma Prohibited Use Policy.
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View details ↗Nemotron-3-Diarization
NVIDIA**Nemotron 3 Diarization** is an open-weight speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference and handles up to eight speakers. Following Sortformer [\[1\]](#ref-1), the model resolves speaker permutation by ordering its output channels according to each speaker's first arrival in the input audio. For streaming inference, the model adopts the Arrival-Order Speaker Cache (AOSC) and FIFO queue introduced in **Streaming Sortformer** [\[2\]](#ref-2). The AOSC retains speaker information from earlier chunks to preserve speaker identities over time, while the FIFO queue provides recent frame context for each processing step. A single checkpoint supports input buffer latency as low as 80 ms for latency-critical applications, although the lowest recommended configuration is 0.32 s; the offline-style configuration uses a 30.4 s input buffer. The output frame resolution is configurable in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.
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View details ↗Nemotron-3-Embed-1B-BF16
NVIDIA**Nemotron-3-Embed-1B-BF16** is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese. The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching. Among models of comparable size, **Nemotron-3-Embed-1B-BF16** achieves state-of-the-art performance across multiple multilingual retrieval benchmarks. Read more details in our Blog Post.
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View details ↗**Nemotron-3-Embed-1B-NVFP4** is the quantized version of the Nemotron-3-Embed-1B-BF16 model, which is developed for text question-answering retrieval. For more information, please check here. The **Nemotron-3-Embed-1B-NVFP4** model is quantized with NVIDIA Model Optimizer, using nvidia-modelopt v0.45.0. This model was evaluated on 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese. Read more details in our Blog Post. This model and its associated configuration files are licensed under the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). ADDITIONAL INFORMATION: Apache License, Version 2.0.
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View details ↗Nemotron-3-Embed-8B-BF16
NVIDIA**Nemotron-3-Embed-8B-BF16** is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese. The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching. **Nemotron-3-Embed-8B-BF16** achieves state-of-the-art performance on the multilingual RTEB leaderboard) as of July 16, 2026. Read more details in our Blog Post.
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View details ↗Nemotron 3 Nano 30B A3B
NVIDIANVIDIA Nemotron 3 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems. The model is fully...
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$0.06 / M input tokensOpenRouter · nvidia/nemotron-3-nano-30b-a3b
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View details ↗NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family. This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below. Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement
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View details ↗NVIDIA Nemotron™ 3 Nano Omni is a 30B-A3B open multimodal model designed to function as a perception and context sub-agent in enterprise agent systems. It accepts text, image, video, and...
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$0 / M input tokensOpenRouter · nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free
View details ↗NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family. This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below. Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement
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View details ↗Nemotron 3 Super
NVIDIANVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...
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$0.3 / M input tokensOpenRouter · nvidia/nemotron-3-super-120b-a12b
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View details ↗Nemotron 3 Ultra
NVIDIANVIDIA Nemotron 3 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...
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$0.6 / M input tokensOpenRouter · nvidia/nemotron-3-ultra-550b-a55b
View details ↗Nemotron 3 Ultra (free)
NVIDIANVIDIA Nemotron 3 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...
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$0 / M input tokensOpenRouter · nvidia/nemotron-3-ultra-550b-a55b:free
View details ↗Nemotron-4-340B-Base
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View details ↗Nemotron-4-340B-Instruct
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View details ↗Nemotron-4-340B-Reward
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