NVIDIA has released Nemotron 3.5 Transcribe, an open-weight 30B MoE model with approximately 4B active parameters. The model supports transcription in 40 languages, speaker attribution, translation, contextual prompting, and audio understanding, and ranks first in VoiceArena's 14-system comparison of speaker diarization plus ASR.
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On October 28, NVIDIA released Nemotron 3.5 Transcribe, an open-weight speech model that can transcribe audio in 40 languages, translate speech, and identify speakers, and developers can control the transcription format with natural language instructions without retraining. The model consists of a Transformer speech encoder, a speaker diarization model, and the Nemotron 3.5 Lightning language model, with approximately 4.3 billion activated parameters, and the released checkpoint is labeled 30B-A4B. NVIDIA says it achieves an average word error rate of 4.71% on nine public test sets of the English Open ASR Leaderboard and 4.62% on seven languages of the multilingual leaderboard, and calls it the best open-weight model on both leaderboards; the model is released in BF16, FP8, and NVFP4 formats as well as GGUF files, under the OpenMDW-1.1 license.
On October 28, NVIDIA released Nemotron 3.5 Transcribe, an open-weight speech model that can transcribe audio in 40 languages, translate speech, and identify speakers, and developers can control the transcription format with natural language instructions without retraining. The model consists of a Transformer speech encoder, a speaker diarization model, and the Nemotron 3.5 Lightning language model, with approximately 4.3 billion activated parameters, and the released checkpoint is labeled 30B-A
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On October 28, 2026, NVIDIA released the speech transcription and translation model Nemotron 3.5 Transcribe, supporting 40 languages including Japanese and capable of transcribing recordings up to 1 hour long. The model is an open-weight 30B MoE architecture that activates approximately 4B parameters during inference, can specify via text instructions whether to retain filler words and how numbers are written, and supports speaker diarization for up to 8 people and bidirectional Japanese-English translation. The weights are released on Hugging Face as NVIDIA-Nemotron-3.5-Transcribe-30B-A4B under the OpenMDW-1.1 license, with BF16, FP8, and NVFP4 versions and NeMo Speech fine-tuning tools provided.
On October 28, 2026, NVIDIA released the speech transcription and translation model Nemotron 3.5 Transcribe, supporting 40 languages including Japanese and capable of transcribing recordings up to 1 hour long. The model is an open-weight 30B MoE architecture that activates approximately 4B parameters during inference, can specify via text instructions whether to retain filler words and how numbers are written, and supports speaker diarization for up to 8 people and bidirectional Japanese-English
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NVIDIA has released Nemotron 3.5 Transcribe, an open-weight large speech model that supports 40 languages and up to 1 hour of audio per pass, and can select output formats such as verbatim, written, speaker-labeled, contextual, and translation via text instructions. The model has approximately 34B total parameters and about 4.3B activated parameters, adopts a hybrid Mamba-Transformer MoE architecture, can run in BF16 on a single H100 80GB, and the NVFP4 version at about 21GB can run on an RTX 5090. NVIDIA self-reports an average WER of 4.71% across 9 public English Open ASR test sets, the best result among open-weight models, though commercial APIs still lead; its shortcomings are that it only supports offline operation, and the Chinese far-field meeting cpCER is 34.95%.
NVIDIA has released Nemotron 3.5 Transcribe, an open-weight large speech model that supports 40 languages and up to 1 hour of audio per pass, and can select output formats such as verbatim, written, speaker-labeled, contextual, and translation via text instructions. The model has approximately 34B total parameters and about 4.3B activated parameters, adopts a hybrid Mamba-Transformer MoE architecture, can run in BF16 on a single H100 80GB, and the NVFP4 version at about 21GB can run on an RTX 50
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NVIDIA has released the open-weight speech transcription model Nemotron 3.5 Transcribe, with 30B total parameters and 4B activated parameters. On Artificial Analysis's non-streaming speech transcription leaderboard, it achieves a 2.8% AA-WER, tying for first among open-source models with Mistral's Voxtral Small. On the speech agent dialogue dataset AA-AgentTalk, it leads Voxtral Small's 2.3% with 1.8%. Its activated parameters are 4B, while Voxtral Small's are 24B; other open-source models of comparable scale have a WER of about 4%.
NVIDIA has released the open-weight speech transcription model Nemotron 3.5 Transcribe, with 30B total parameters and 4B activated parameters. On Artificial Analysis's non-streaming speech transcription leaderboard, it achieves a 2.8% AA-WER, tying for first among open-source models with Mistral's Voxtral Small. On the speech agent dialogue dataset AA-AgentTalk, it leads Voxtral Small's 2.3% with 1.8%. Its activated parameters are 4B, while Voxtral Small's are 24B; other open-source models of co
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Nvidia has released the open-weight multilingual transcription model Nemotron 3.5 Transcribe, which ranks first on the Hugging Face Open ASR English open-source model leaderboard with an average word error rate of 5.33%. The model uses a 30B MoE architecture with 4B activated parameters, supports transcription in 40 languages and translation between 31 languages and English, can label speakers and distinguish up to 8 voices, handles recordings up to 1 hour long, but does not support real-time streaming. Users can provide instructions such as context and terminology before transcription; the model can be deployed with vLLM or run locally on a single GPU via NeMo-Speech.cpp; Nvidia has also released the data curation pipeline Granary v2 and a public manifest of approximately 463,062 hours across 48 languages.
Nvidia has released the open-weight multilingual transcription model Nemotron 3.5 Transcribe, which ranks first on the Hugging Face Open ASR English open-source model leaderboard with an average word error rate of 5.33%. The model uses a 30B MoE architecture with 4B activated parameters, supports transcription in 40 languages and translation between 31 languages and English, can label speakers and distinguish up to 8 voices, handles recordings up to 1 hour long, but does not support real-time st
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NVIDIA has released the open-weight model Nemotron 3.5 Transcribe on Hugging Face and ModelScope, integrating transcription, speaker diarization, translation, and audio question answering into a single checkpoint. It supports transcription in 40 languages, translation between 31 languages and English, and speaker labeling in 13 languages, can process recordings up to 1 hour long, and can label up to 8 speakers. The model ranks first among open-weight models on the English leaderboard of the Hugging Face Open ASR Leaderboard with an average WER of 5.33%, and achieves 2.8% AA-WER on Artificial Analysis with approximately 4B activated parameters. The simultaneously released Granary v2 covers 48 languages and 463,062 hours of speech data, and the accompanying NeMo Speech fine-tuning recipe reduced WER on held-out speakers from 22.44% to 12.17% in a medical case.
NVIDIA has released the open-weight model Nemotron 3.5 Transcribe on Hugging Face and ModelScope, integrating transcription, speaker diarization, translation, and audio question answering into a single checkpoint. It supports transcription in 40 languages, translation between 31 languages and English, and speaker labeling in 13 languages, can process recordings up to 1 hour long, and can label up to 8 speakers. The model ranks first among open-weight models on the English leaderboard of the Hugg
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Nvidia has released the free open-weight model Nemotron 3.5 Transcribe, which can transcribe audio in up to 40 languages, and simultaneously launched version 2 of the open speech dataset Granary. The model supports translation between 31 languages and English, processes recordings up to one hour long, and distinguishes up to 8 speakers; Nvidia claims it outperforms competing products on benchmarks such as the Hugging Face Open ASR Leaderboard. The model has 30 billion parameters and can run on Jetson AGX Thor, DGX Spark, and Blackwell data center chips, but it only supports recorded audio and does not support live streams.
Nvidia has released the free open-weight model Nemotron 3.5 Transcribe, which can transcribe audio in up to 40 languages, and simultaneously launched version 2 of the open speech dataset Granary. The model supports translation between 31 languages and English, processes recordings up to one hour long, and distinguishes up to 8 speakers; Nvidia claims it outperforms competing products on benchmarks such as the Hugging Face Open ASR Leaderboard. The model has 30 billion parameters and can run on J
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NVIDIA releases Nemotron-3.5-Bolt-E4B, which can run locally on devices with 6GB of memory, targeting on-device devices such as phones. The model is trained based on Gemma 4, supports vision, and Unsloth also provides GGUF files and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools.
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NVIDIA has released Kumo Relational, a pretrained relational foundation model that can make predictions directly on enterprise databases in a single forward pass, without task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases—RelBench V1/V2, SAP SALT, and 4DBInfer—it outperforms supervised relational deep learning models and gradient-boosted tree pipelines built with expert feature engineering, achieving an 8% higher MRR than the AutoGluon ensemble on SAP SALT. The model is available through NVIDIA's open-source GPU-native structured-data-models library or NVIDIA NIM, and supports databases with 500B+ rows.
NVIDIA has released Kumo Relational, a pretrained relational foundation model that can make predictions directly on enterprise databases in a single forward pass, without task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases—RelBench V1/V2, SAP SALT, and 4DBInfer—it outperforms supervised relational deep learning models and gradient-boosted tree pipelines built with expert feature engineering, achieving an 8% higher MRR than the AutoGluon ensemble on S
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On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100; the open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM as well as Baseten, DeepInfra, and FriendliAI.
On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retri
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NVIDIA has added three models to the Nemotron open-source model family, covering enterprise workflows, local AI agents, and conversational applications across data centers, desktops, and edge devices. These include the 120-billion-parameter Nemotron 3.5 Super VL, which NVIDIA claims delivers up to 5x the throughput of the next-best comparable model, with accuracy approaching that of its largest reasoning model at roughly one-fifth the parameter count; there are also two small models in the Nemotron 3.5 Bolt series: 74B-A9B 24GB for everyday agent tasks on RTX and DGX Spark, and E4B 4GB for conversational and physical AI agents. Super VL and Bolt 74B-A9B are already available, and Bolt E4B is scheduled for release on October 23.
NVIDIA has added three models to the Nemotron open-source model family, covering enterprise workflows, local AI agents, and conversational applications across data centers, desktops, and edge devices. These include the 120-billion-parameter Nemotron 3.5 Super VL, which NVIDIA claims delivers up to 5x the throughput of the next-best comparable model, with accuracy approaching that of its largest reasoning model at roughly one-fifth the parameter count; there are also two small models in the Nemot
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NVIDIA Nemotron 3.5 Super VL is now available on OpenRouter. It is an open-source 120B-A12B multimodal model designed for long-running agents that need to observe, reason, and act across text, images, documents, GUIs, and video, with support for a 1M context. The model uses a Hybrid Mamba-Transformer MoE architecture and supports multi-token prediction to speed up output, targeting scenarios such as document intelligence, web research, customer service, and video understanding.
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