NVIDIA AI

GTC Golden Tickets are the best! Love creating unforgettable experiences. Congrats again to our #NVIDIAGTC Berlin Golden Ticket winners that were treated like true VIPs, from their special keynote seating to exclusive events and everything in between. And a huge thank you to the judges @asierarranz @choroukmalmoum @SteveNouri @mervenoyann @itsjohnnynunez and @googlecloud

Sound on 🔊 Listen to the difference. Our new open multilingual NemotronLabs RE-USE model from NVIDIA researchers cleans up background noise, echo and other audio distortions to make speech clearer. All with just 9.6M parameters. Try the demo and download the model on @huggingface → huggingface.co/nvidia/NVIDIA…

How do you make a chip design agent more reliable and efficient? The @Siemens EDA team used NVIDIA NeMo Gym to check the engineering files its agent produced, trace errors, and improve the agent’s skills and tool descriptions. In its standard cell characterization benchmark, those changes raised the composite verification score from ~55% to ~95%, with 66% fewer agent turns and 64% fewer output tokens. We just published a new techblog that walks through what the team measured, what they changed, and how other engineering teams can apply the approach → nvda.ws/4Beujcb

Every extra search an AI agent makes uses more tokens. Watch Nemotron Retriever tackle the same questions as another open-source retrieval stack. Check out the evidence each retrieves, accuracy and token usage. Get the open models and recipes on @huggingface → huggingface.co/blog/nvidia/n…

On @ArtificialAnlys, Nemotron 3.5 Transcribe achieves a 2.81% word error rate with ~4B active parameters. Voxtral Small scores 2.77% with 24B. Nearly the same transcription accuracy, with about one-sixth the active parameters. Benchmark → artificialanalysis.ai/speech… 5/

Who said what? Nemotron 3.5 Transcribe ranks #1 in VoiceArena’s 14-system diarization + ASR comparison and #1 on David AI’s multilingual diarization benchmark. 4/

Give it a recording. Tell it what you need. Add speaker labels to a transcript. Translate it into Spanish. Then Japanese. Ask it to find and redact PII data. Here’s what that looks like with a meeting recording. 3/

Nemotron 3.5 Transcribe ranks #1 among open-weight models on the @huggingface Open ASR English leaderboard with a 5.33% average word error rate. Download the model → huggingface.co/nvidia/NVIDIA… Learderboard → huggingface.co/spaces/hf-aud… 2/

Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵

How do you assess cyclone risk beyond what a few decades of historical data can reveal? @AXA researchers are using NVIDIA Earth-2 to help fill that gap. Using FourCastNet 3, one of Earth-2’s open, global weather forecasting models, the team generated more than 1,000 simulated cyclone seasons to study possible storm activity around the globe. These simulations help researchers understand where cyclones travel, how often they occur, and how storms affect multiple regions within the same season. Those insights can inform AXA’s risk assessments and help customers target prevention efforts. Learn how AXA and other organizations are applying Earth-2 to weather forecasting, risk assessment, and environmental modeling: nvda.ws/4hPfG7z