Nvidia Introduces Nemotron 3, as Open AI Model Competition Heats Up

On December 15th, 2025, Nvidia launched Nemotron 3 a new open model family, designed for writing, coding and agentic purposes. The company released Nemotron 3 Nano immediately and indicated two other variants, Nemotron 3 Super and Nemotron 3 Ultra, will arrive in the first half of 2026

The timing matters because “open” is no longer just a developer preference but a need. Reuters reported on the emergence of widely used open models like Deepseek and Qwen 3 developed in Chinese labs and with the reality that organizations are increasingly under pressure concerning which models they deploy.

Nvidia’s pitch for Nemotron 3 is that it’s an “open, transparent” option for companies and government agencies, so they can use it without being locked into a closed provider or model they might have to abandon later.

Nemotron 3 Nano’s bet: on speed and context

Nvidia’s thesis is simple: Most AI models don’t fall short because they’re “not smart enough,” but because they can’t handle big workflows cheaply or reliably. Nemotron 3 Nano tackles that by cutting down the compute needed per token and allowing larger context windows.

Important specifications highlighted by Nvidia for Nemotron 3 Nano include:

Scale: It has about 31.6B parameters in total, but only around 3.2B are used on each pass. Because it follows a Mixture-of-Experts design, only a small part of the model is active for each input token, which makes it much more efficient.

Context: up to 1 million tokens, with the goal of holding more of a project "in memory" so an agent will not have to continually repeat summarizations of threads, documents, and instructions.

Scale of training: pretrained with 25 trillion tokens, implying wide exposure to language and code without being narrowly focused in a single domain.

Speed: Nvidia claims a speed increase of about 4x over Nemotron 2 Nano, and tests have demonstrated increased throughput performance over a competitive model.

As Nvidia explains, Nano represents a Mamba + Transformer hybrid model featuring MoE. Breaking it down: it’s an attempt to maintain transformer levels of quality with a state space processing approach Mamba-style processing, which can keep time complexity constant for each new token being added, such that inference performance isn't affected negatively when dealing with longer conversations/documents.

Benchmark materials released with this launch describe competitive performance in reasoning, coding, and long context tests (including RULER with long contexts up to 1M).

Why Nemotron 3 matters in automation

In real-world automation, "agentic" is relevant only if the system can survive messy tasks such as tool calls, long docs, handoffs, and backtracking. Nemotron 3’s positioning relates to problem points where agents struggle most:

Sales & support: keep a full thread of customers during research, writing, updating CRM fields, and adhering to policies without neglecting previous limits.

Marketing: Brief content, context of previous marketing campaigns, and brand guidelines must remain constant in order to produce variations quickly.

Legal & Compliance: Reduce information loss when navigating a lengthy contract, exhibit, or policy library where a superficial summary may be dangerous.

Healthcare ops & industry: Handling SOP-rich workflows such as triage, scheduling, procurement, and maintenance where a sequence of lengthy procedures need to be read and executed by an agent.

The larger arc is what’s to come. Nvidia speaks of Super and Ultra in H1 2026 and mentions efficiency gains such as NVFP4 in their strategy to make bigger models more affordable to train and to operate.

If these product launches go according to plan and lead to reliable deployed AI, Nemotron 3 might become the new norm in open-source software stacks for companies already using Nvidia hardware.