Uploaded June 2026 | Updated September 2026, 2 weeks ago
Nemotron 3 Ultra is NVIDIA's latest frontier-intelligence open model — 5x faster inference, up to 30% lower cost, and fully open: weights, training datasets, and fine-tuning recipes included. In this livestream, we're joined by Nathan Lambert, ML researcher and open model advocate, to dig into what Ultra means for developers building on open models today.
We'll cover what sets Ultra apart technically — the hybrid Mamba-Transformer backbone, Multi-Teacher On-Policy Distillation (MOPD), and how it fits into a system-of-models pattern. Nathan brings a researcher's perspective on post-training for agentic systems, and we'll get into where the open frontier model landscape is heading and what it takes to build models worth building on.
What you'll learn:
- How Ultra's post-training approach compares to what the open model ecosystem has seen at scale
- What the hybrid Mamba-Transformer architecture means for long-context, multi-turn agent workflows
- How open weights, datasets, and recipes enable domain-specific fine-tuning from day one
- Where open frontier models are heading for agentic applications — and what tradeoffs matter most
Have questions about Ultra, post-training, or the open model landscape? Drop them live — Nathan and the team will answer them in real time.
Nemotron 3 Ultra is NVIDIA's latest frontier-intelligence open model — 5x faster inference, up to 30% lower cost, and fully open: weights, training datasets, and fine-tuning recipes included. In this livestream, we're joined by Nathan Lambert, ML researcher and open model advocate, to dig into what Ultra means for developers building on open models today.
We'll cover what sets Ultra apart technically — the hybrid Mamba-Transformer backbone, Multi-Teacher On-Policy Distillation (MOPD), and how it fits into a system-of-models pattern. Nathan brings a researcher's perspective on post-training for agentic systems, and we'll get into where the open frontier model landscape is heading and what it takes to build models worth building on.
What you'll learn:
- How Ultra's post-training approach compares to what the open model ecosystem has seen at scale
- What the hybrid Mamba-Transformer architecture means for long-context, multi-turn agent workflows
- How open weights, datasets, and recipes enable domain-specific fine-tuning from day one
- Where open frontier models are heading for agentic applications — and what tradeoffs matter most
Have questions about Ultra, post-training, or the open model landscape? Drop them live — Nathan and the team will answer them in real time.
