NVIDIA

NVIDIA has released Cosmos 3 Edge, a 4-billion-parameter open world model designed for edge AI systems. The model delivers real-time robot control at 15 Hz on NVIDIA Jetson devices, generating 32 actions per inference at 640×360 resolution.

NVIDIA has released Nemotron-3-Embed-1B-BF16, a 1.14 billion parameter text embedding model supporting 34 languages with a 32,768 token context window. The model generates 2048-dimensional embeddings and was derived from Ministral-3-3B-Instruct-2512 through two rounds of structured pruning and distillation, first to 2B then to 1.14B parameters.

NVIDIA and Hugging Face have integrated NeMo Automodel with the Diffusers library, enabling distributed fine-tuning of video and image diffusion models without checkpoint conversion. The integration supports models including FLUX.1-dev (12B), Wan 2.1 (1.3B/14B), and HunyuanVideo (13B) with full fine-tuning and LoRA options.

NVIDIA's Nemotron-3-Embed-8B-BF16 ranks #1 on the RTEB leaderboard with a 78.5% score, while the 1B variant reduces error rate by 27% over its predecessor. The open-weight models feature 32k context windows and production-ready deployment options including a Blackwell-optimized NVFP4 variant.

Nvidia released Cosmos 3 Edge, a world model designed for robots and vision AI agents to perceive and navigate physical environments in real time. The company announced partnerships with Japanese industrial giants including Fujitsu, Hitachi, and Kawasaki Heavy Industries as part of its physical AI expansion.

Amazon SageMaker AI now supports serverless fine-tuning for NVIDIA Nemotron 3 Nano (30B parameters, 3B active) and Nemotron 3 Super (120B parameters, 12B active). The integration includes supervised fine-tuning, reinforcement learning with verifiable rewards (RLVR), and reinforcement learning from AI feedback (RLAIF).

NVIDIA has released Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super that reduces the model from 120.7B total/12.8B active parameters to 75.3B total/9.3B active parameters. According to NVIDIA, the model achieves approximately 2× higher server throughput on a single 8×B200 node and increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests while maintaining strong accuracy across benchmarks.

NVIDIA released Audex-30B-A3B, a unified audio-text model built on the Nemotron-Cascade-2-30B-A3B backbone. The model handles audio understanding, speech recognition and translation, text-to-speech, audio generation, and speech-to-speech while supporting up to 1M token context length.

NVIDIA has released over 10 trillion pre-training tokens and millions of post-training samples as part of its Nemotron open data initiative for building AI agents. The release includes the Nemotron Post-Training v3 Prompt Atlas, an interactive visualization tool, and Nemotron-Personas dataset representing 2.4 billion people across 10 countries.

NVIDIA released Nemotron-Labs-TwoTower-30B-A3B-Base-BF16, a block-wise diffusion language model that generates text by denoising blocks of tokens in parallel rather than sequentially. According to NVIDIA, the model achieves 2.42× the wall-clock generation throughput of its autoregressive baseline while retaining 98.7% of aggregate benchmark quality.

Amazon Bedrock now supports NVIDIA Nemotron and OpenAI GPT OSS models in AWS GovCloud (US) Regions. The launch includes OpenAI's GPT OSS models (120B and 20B parameters, 128K context) and NVIDIA Nemotron 3 family (9B to 120B parameters, 1M context), providing government agencies FedRAMP High and DoD SRG Level 5-compliant AI inference on U.S. soil.

Nvidia has released Nemotron 3 Ultra, a 550-billion parameter mixture-of-experts model with 55 billion active parameters and support for up to 1 million token context windows. The model uses a hybrid Transformer-Mamba architecture and is designed specifically for long-running agentic workflows including agent orchestration, coding agents, and complex enterprise tasks.

NVIDIA released Nemotron-3-Ultra-550B-A55B-NVFP4, a 550B parameter model with 55B active parameters, featuring a 1M token context window and configurable reasoning mode. The model uses a hybrid LatentMoE architecture combining Mamba-2, Mixture-of-Experts, and Attention layers with Multi-Token Prediction, trained with NVIDIA's NVFP4 quantization-aware approach.

NVIDIA released Nemotron-3-Ultra-550B, a frontier-scale model with 550B total parameters (55B active) and up to 1M token context window. The model uses a hybrid LatentMoE architecture combining Mamba-2, MoE, and attention layers with Multi-Token Prediction, trained with NVFP4 quantization-aware methods from December 2025 to April 2026.

NVIDIA has released Nemotron 3.5 Content Safety, a 4B-parameter model built on Google Gemma 3 4B IT that provides multimodal safety classification across approximately 140 languages. The model includes a 128K context window, custom enterprise policy enforcement, auditable reasoning traces, and is releasing its training dataset.

NVIDIA Nemotron 3 Ultra is now available on Amazon SageMaker JumpStart with 550 billion total parameters and 55 billion active parameters. The model features a hybrid Transformer-Mamba Mixture-of-Experts architecture and supports context windows up to 1 million tokens, targeting agentic AI workloads.

Nvidia has released Nemotron 3.5 Content Safety, a 4-billion parameter multimodal guardrail model fine-tuned from Google Gemma-3-4B. The model is available for free, supports 128K token context windows, and moderates content across 12 languages.

Nvidia has released Nemotron 3 Ultra, a 550B parameter mixture-of-experts model with 55B active parameters and a 1M token context window. The model uses a hybrid Transformer-Mamba architecture and is available for free through OpenRouter, targeting agentic workflows and multi-step reasoning tasks.

NVIDIA released Nemotron 3.5 ASR, a 600M-parameter speech-to-text model supporting 40 language-locales from a single checkpoint. The model achieves 0.07 seconds to final transcript after speech ends and ranks 2nd in latency among streaming ASR models according to Artificial Analysis benchmarks.

NVIDIA demonstrated that task-seeded synthetic Q&A data improves model performance across multiple benchmarks in a 100B-token continuation experiment on Nemotron-3 Nano. The approach improved GPQA scores by +11.1 points, MMLU-Pro by +1.8, average code by +1.9, and commonsense understanding by +1.6.