DeepInfra Partners with NVIDIA to Launch Nemotron 3 Nano Reasoning Model

DeepInfra has announced its role as an official launch partner for NVIDIA Nemotron 3 Nano, the first in a new family of open reasoning models. This collaboration brings immediate, serverless access to a model specifically designed for agentic AI and complex reasoning workflows. Developers can now deploy high-performance reasoning without the burden of managing GPU clusters or dealing with long configuration times. The Nemotron 3 Nano introduces a specialized architecture that blends Mamba sequence processing with Transformer-based Mixture-of-Experts. This hybrid approach allows the model to handle a massive one-million-token context window while maintaining linear scaling. By activating only a small fraction of its total parameters during inference, it provides high throughput at a much lower cost than traditional dense models.

NVIDIA focused the training of this model on reasoning capabilities rather than simple text generation. It utilizes curated synthetic datasets and reinforcement learning to perform deep analysis in fields like mathematics and coding. These reasoning traces allow the AI to think through a problem step-by-step before delivering a final answer, making it a powerful tool for autonomous agents.

Security remains a primary focus for this deployment on DeepInfra. The platform operates under a strict zero-retention policy, meaning user data is never stored or used for future model training. With SOC 2 and ISO 27001 certifications, the infrastructure is built to support sensitive enterprise reasoning tasks that require high levels of privacy and compliance.

This release marks the beginning of a broader rollout for the Nemotron 3 series throughout the coming year. Larger iterations, including the Super and Ultra versions, are expected to follow, providing even more intelligence for heavy-duty enterprise tasks. This partnership ensures that open-source developers have a direct path to the latest advancements in reasoning technology as they emerge.

To help users get started, DeepInfra has provided a detailed tutorial notebook covering the basics. It guides developers through parameter tuning and long-context handling to optimize the model for specific use cases. This resource aims to shrink the time between initial idea and production-ready deployment for teams of all sizes.