NVIDIA

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 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.

Anthropic has launched the public beta of Claude Science, an AI workbench for scientific research that integrates NVIDIA's BioNeMo Agent Toolkit. The platform allows scientists to execute end-to-end research workflows using natural language commands to interact with digital agents.

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 released a quantized version of Google DeepMind's DiffusionGemma 26B A4B IT, a multimodal model with 25.2B total parameters (3.8B active) that processes text, image, and video inputs. The NVFP4-quantized model achieves generation speeds exceeding 1,100 tokens per second on NVIDIA H100 GPUs while supporting a 256K token context window.

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 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 released Cosmos3-Super-Text2Image, a 64-billion parameter text-to-image generation model as part of its Cosmos3 collection of omnimodal world models. The model uses a Mixture-of-Transformers architecture combining autoregressive and diffusion transformers, designed for Physical AI applications including robotics and autonomous vehicles.

NVIDIA released Nemotron-Labs-Diffusion-14B, a 14-billion parameter language model that supports three decoding modes by switching attention patterns during inference. The model achieves 850 tokens per second on GB200 hardware at concurrency 1, representing a 3.3x speed-up over standard autoregressive decoding and outperforming Qwen3-8B-Eagle3 by 2.2x in self-speculation mode.

NVIDIA released Nemotron-3-Nano-Omni-30B-A3B, a multimodal large language model with 31 billion parameters that processes video, audio, images, and text with up to 256K token context. The model uses a Mamba2-Transformer hybrid Mixture of Experts architecture and supports chain-of-thought reasoning mode.

NVIDIA released Nemotron 3 Nano Omni, a 31B parameter (30B active, 3B per token) multimodal model supporting video, audio, image, and text inputs. The model features a 256K token context window, reasoning mode with chain-of-thought, and tool calling capabilities.

OpenAI released GPT-5.5 on April 23, its first retrained base model since GPT-4.5, scoring 82.7% on Terminal-Bench 2.0 versus GPT-5.4's 75.1% and Claude Opus 4.7's 69.4%. API pricing is set at $5 per million input tokens and $30 per million output tokens, exactly double GPT-5.4 rates.

NVIDIA has released Nemotron 3 Nano Omni 30B-A3B, a multimodal large language model with 31 billion parameters using a Mamba2-Transformer hybrid Mixture of Experts architecture. The model supports video, audio, image, and text inputs with a 256K token context window and includes a dedicated reasoning mode with chain-of-thought capabilities.

NVIDIA has launched Nemotron 3 Nano Omni on Amazon SageMaker JumpStart, a multimodal model with 30 billion total parameters (3 billion active) that processes video, audio, images, and text in a single inference pass. The model features a 131K token context window and uses a Mamba2 Transformer Hybrid MoE architecture combining three specialized encoders.

NVIDIA released Nemotron 3 Nano Omni, a 30B-A3B Mixture-of-Experts model that processes text, images, video, and audio. The model uses a hybrid Mamba-Transformer architecture with 128 experts and achieves 65.8 on OCRBenchV2-En and 72.2 on Video-MME, while delivering up to 9x higher throughput on multimodal tasks compared to alternatives.

OpenAI has released GPT-5.5, its latest frontier model, according to NVIDIA. The model powers Codex, OpenAI's agentic coding application, running on NVIDIA GB200 NVL72 rack-scale systems.

NVIDIA engineer Asier Arranz demonstrated Gemma 4 running as a vision-language agent (VLA) on a Jetson Orin Nano Super with 8GB RAM. The model autonomously decides when to access a webcam based on user queries, with no hardcoded triggers—performing speech-to-text, vision analysis, and text-to-speech entirely locally.

NVIDIA released Nemotron-Personas-Korea, a dataset containing 7 million demographically accurate synthetic personas grounded in official Korean statistics from KOSIS, Supreme Court of Korea, and the National Health Insurance Service. The dataset includes 26 fields per persona covering demographics, geography, and occupation across all 17 Korean provinces, with zero personally identifiable information under CC BY 4.0 license.

NVIDIA released GR00T N1.7, a 3-billion parameter open-source Vision-Language-Action model for humanoid robots with commercial licensing. The model was trained on 20,854 hours of human egocentric video data and demonstrates the first documented scaling law for robot dexterity, where increasing human video data from 1,000 to 20,000 hours more than doubles task completion rates.

NVIDIA released Nemotron-3-Nano-4B-GGUF on March 16, 2026, a 4-billion parameter small language model (SLM) designed for edge deployment on devices like Jetson Thor and GeForce RTX. The model features a hybrid Mamba-2 and Transformer architecture with a 262K token context window and supports both reasoning and non-reasoning modes via system prompts.

ABB and NVIDIA have partnered to deploy physical AI simulation in factory automation, addressing the critical sim-to-real gap that has limited intelligent robotics deployment. The approach uses digital physics simulation to train models that transfer reliably to actual factory floors, reducing production hurdles and securing measurable ROI.