NVIDIA is taking another giant step towards becoming the absolute ruler of the industry, not only in hardware but also in software and artificial intelligence models. The company’s newest open source artificial intelligence model Nemotron-3 SuperAccording to the latest published performance data, it is at the top of the open source world.

This massive model with 120 billion parameters outperforms strong competitors like DeepSeek and GPT-OSS, especially on demanding platforms like EnterpriseOps Gym that test enterprise workflows and complex reasoning processes. Optimized for NVIDIA’s Blackwell GPU architecture, this model sets a new standard in the era of artificial intelligence agents with its high processing speed and huge context window of 1 million tokens.

Maximum Efficiency with Hybrid Architecture: What Does Nemotron-3 Super Offer?

NVIDIA Nemotron-3 Super features innovative technology in its technical infrastructure. Hybrid Mamba-Transformer MoE (Mixture-of-Experts)It attracts attention with its architecture. The model, which has a total of 120 billion parameters, provides tremendous energy and process efficiency by using only 12 billion active parameters during the process. This “blend of experts” approach allows the model to run only the relevant parts of each word when producing it.

One of the biggest trump cards of the model is the graphics card developed by NVIDIA for the new generation Blackwell (B200) graphics cards. NVFP4 (4-bit floating point)It supports the format natively.

Thanks to this technology, the memory requirement is significantly reduced, while the processing speed increases up to 5 times compared to previous generations. Trained on a massive dataset of 25 trillion tokens, Nemotron-3 Super demonstrates high accuracy in a wide range of tasks, from complex coding tasks to cybersecurity analysis.

Big Race with Rivals: DeepSeek and GPT-OSS Left Behind

Competition in the open source artificial intelligence market is fiercer than ever. Especially based in China DeepSeekV3.2 of and a favorite of the open source community GPT-OSS 120B, had been challenging the leadership seat for a long time. However, in the EnterpriseOps Gym benchmark tests, Nemotron-3 Super scores 27.3 points, pushing all its Competitors to the bottom.

At this point, it is necessary to look at the situation of the Competitors from an objective perspective. Meta’s Llama 4series and Google’s Gemma 3models are still very strong alternatives in terms of general usage and chat capabilities.

Llama 4, in particular, continues to be the first choice of many developers with its wide community support and flexible licensing structure. However, when it comes to “artificial intelligence agents” (AI agents) and corporate tool use, the tool calling ability and low margin of error offered by NVIDIA’s model puts it one step ahead in professional use.

1 Million Token Context Window and Agent Capabilities

The “memory” problem, which is the biggest problem of artificial intelligence models in the corporate world, becomes history with Nemotron-3 Super. The model offers 1 million token context windowThanks to it, it can keep thousands of pages of documents, past e-mails and complex technical manuals in its memory at once.

This provides a critical advantage, especially for autonomous artificial intelligence agents that pursue long-term projects.

The model, which achieved a high success rate of 60.47% in SWE-Bench Verified tests, can solve real-world GitHub problems by working like a software engineer.

With this move, NVIDIA once again proves that it is not just a GPU manufacturer, but the technology giant that achieves best hardware-software integration (full-stack). The company leverages the power of the community by openly sharing its models on platforms such as Hugging Face.

NVIDIA’s Ecosystem Strategy Pays off

This success of NVIDIA is not a coincidence. Company; It connects its users more tightly to its ecosystem with CUDA libraries, TensorRT optimization tools and now Nemotron models.

Nemotron-3 Super is a solution that reduces costs, especially for data centers and cloud providers, with its high throughput and low latency. This race with giants such as Llama and Mistral in the open source world ultimately returns to the end user as smarter, faster and more reliable artificial intelligence tools.