Nvidia Releases Nemotron 3: Open Reasoning Models Optimized for Agentic AI

Three Model Sizes for Every Use Case

Nemotron 3 arrives in three distinct sizes, each optimized for different deployment scenarios in agentic AI systems:

30B

Nemotron 3 Nano

• Edge deployment ready
• 4x throughput improvement
• Efficient inference
• Real-time agent applications

100B

Nemotron 3 Super

• Enterprise workloads
• Complex reasoning tasks
• Multi-step planning
• Production agentic systems

500B

Nemotron 3 Ultra

• Research-grade capability
• 1M token context window
• State-of-the-art reasoning
• Frontier agentic applications

What Makes Nemotron 3 Special

4x Higher Token Throughput

The Nano version alone delivers four times the token throughput of its predecessor, making real-time agentic applications viable at scale. This is a game-changer for applications requiring fast, iterative reasoning.

1 Million Token Context Window

The Ultra model supports context windows up to 1 million tokens, enabling agents to maintain coherent reasoning across extremely long conversations, codebases, or document sets.

Open Source with RL Tools

Unlike many frontier models, Nemotron 3 is fully open source and comes with reinforcement learning tools and open datasets for fine-tuning. This democratizes access to agentic AI capabilities.

Built for Agentic AI Systems

Nemotron 3 is explicitly designed for agentic AI - AI systems that can autonomously plan, reason, and execute multi-step tasks. Key capabilities include:

Multi-step Reasoning: Chain-of-thought optimizations for complex problem solving
Tool Use: Native support for function calling and external tool integration
Planning: Hierarchical task decomposition and execution
Self-Correction: Built-in mechanisms for error detection and recovery

Why This Matters: As AI moves from answering questions to taking actions, models need specialized architectures. Nemotron 3 represents Nvidia's bet that reasoning-optimized models will power the next generation of AI agents.

Nvidia Acquires SchedMD (Slurm)

In a strategic move announced alongside Nemotron 3, Nvidia is acquiring SchedMD, the primary commercial developer of Slurm - the world's most widely used job scheduler for HPC and AI workloads.

Why Slurm Matters

• Powers 60%+ of the world's supercomputers
• Critical for managing distributed AI training jobs
• Enables efficient GPU cluster orchestration
• Used by most major AI research labs

This acquisition gives Nvidia end-to-end control over the AI training infrastructure stack: from GPUs to job scheduling to model frameworks.

Developer Takeaways

Open Source Access: Download and fine-tune Nemotron 3 for your agentic applications
Edge Deployment: The 30B Nano model is suitable for edge and on-premise deployments
RL Fine-tuning: Use provided reinforcement learning tools for domain-specific optimization
Long Context: Leverage 1M context for document-heavy or code-heavy agent applications