NVIDIA’s Nemotron-class open source LLM has just been significantly enhanced with the latest release, Nemotron 3 Super, which now targets agent AI workloads with its wide context window.
NVIDIA’s Nemotron 3 Super Leverages Mamba-MoE, With a Vast 1 Million Token Context Window
For those who don’t know, when we talk about the leading contributors in the world of open source AI models, some may think of Chinese AI labs like Kimi or Qwen, but in reality, NVIDIA’s Nemotron series is leading the way in this regard. Because AI is distributed in a “five-layer” pie, NVIDIA not only dominates the infrastructure and chips but is also one of the few Western countries to invest heavily in the open source model. Therefore, NVIDIA has now launched the Nemotron 3 Super, with the main idea of running agent AI applications at scale, making it ideal for agents like OpenClaw.
One of the standout aspects of the Nemotron 3 Super is NVIDIA’s hybrid Mamba-MoE architecture. Compared to traditional MoE models, Mamba is a very impressive implementation. Essentially, NVIDIA has changed the way LLM interprets data streams. With newer architecture, Mamba relies on State Space Model (SSM) to read data linearly, preventing the formation of large context windows and inserting irrelevant information. Mamba-MoE allows Nemotron 3 Super to maintain the optimal context window for the user’s workload, resulting in the best agent response.
- Hybrid Architecture: The Mamba layer provides 4x higher memory and computing efficiency, while the transformer layer drives advanced reasoning.
- Ministry of Foreign Affairs: Only 12 billion of its 120 billion parameters are active in inference.
- Latent MoE: A new technique that improves accuracy by enabling four expert specialists at the cost of one to generate the next token at inference.
- Multi-Token Prediction: Predict multiple future words simultaneously, resulting in 3x faster inference.
– NVIDIA
The Mamba layer delivers 4x higher memory efficiency and advanced reasoning, making Nemotron 3 Super ideal for inference workloads. Another impressive feature of Nemotron 3 Super is the 1 million token context window, which is 4 times larger than Kimi 2.5’s window. There is a general law in agent systems: the larger the window, the better the response. This is why, from this aspect alone, Nemotron 3 Super dominates all other open source LLMs and even comes close to Opus 4.5, despite being limited to only 120 billion parameters.
Speaking of OpenClaw, NVIDIA tested the Nemotron 3 Super in PinchBench, a suite used to evaluate agent workloads, and the model scored 85.6% across the full test suite, outperforming Opus 4.5, Kimi 2.5, and GPT-OSS 120b. For consumers running extensive workloads through OpenClaw, the Nemotron 3 Super has opened up a whole new class of performance, with computing power requirements that can be met with just a single GPU.
Nemotron 3 Super is just an example of how broad agent AI systems will truly advance, and interestingly, LLM is now also addressing computational limitations, which is why the future of deploying models at the edge is brighter than ever.
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