Nvidia Nemotron 3 Super

Our Take

A workable 120B-parameter MoE language model from NVIDIA. Pulls ahead on competition math (AIME 2026) (90/100), so reach for it when that's the dimension that matters.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

• Total Parameters: 120B
• Active Parameters: 12B
• Architecture: Hybrid Mamba-Transformer MoE
• Training Data: 25 trillion tokens
• Inference Acceleration: Native Multi-Token Prediction (MTP) layers for speculative decoding.

The use of Latent MoE is a departure from standard MoE implementations. It optimizes for both accuracy per FLOP and accuracy per parameter, ensuring that the 12B active parameters punch well above their weight class. By integrating Mamba-2 layers, the model handles long sequences more efficiently than pure Transformer models, as Mamba's linear scaling reduces the computational overhead of the 1-million-token context window.

Nemotron 3 Super includes native MTP layers. For local practitioners, this is a major advantage: it enables built-in speculative decoding. This allows the model to predict multiple tokens in a single forward pass, resulting in a 2.2x to 7.5x throughput increase compared to similarly sized dense models when running on NVIDIA hardware.

Nemotron 3 Super is a text-only model tuned for high-logic tasks. It is not a general-purpose "creative writing" model but rather a functional tool for engineers and researchers.

The model is specifically post-trained for agentic workflows using NVIDIA’s Nemotron-post-training-v3 datasets. It excels at:

• Complex Tool Use: Correcting its own mistakes when a function call fails or returns unexpected data.
• Multi-Step Planning: Breaking down a high-level prompt into a sequence of executable sub-tasks.
• Coherence: Maintaining the original objective across a 15x increase in token volume typical of multi-agent "chatter."

With its 120B scale, the model shows high proficiency in Python, C++, and CUDA programming. Because it was pre-trained in NVFP4 (NVIDIA's 4-bit floating point format), the base weights are already optimized for high-precision logic even at lower bit-rates. It is a primary candidate for local RAG (Retrieval-Augmented Generation) over large repositories where the 1M context window allows you to skip complex chunking strategies and simply feed the model the relevant files.

Running a 120B model locally is a significant hardware undertaking. Even though only 12B parameters are active during inference, the entire 120B parameter set must reside in VRAM (or system RAM for GGUF/Mac users) to avoid massive "offloading" bottlenecks.

To run Nemotron 3 Super, your primary constraint is the footprint of the weights.

For most practitioners, Q4_K_M is the "sweet spot." It preserves nearly all the reasoning capabilities of the BF16 original while fitting into the ~80GB VRAM pool common in professional workstations.

If you are trying to run a 120B model on consumer GPUs, you will need at least three RTX 3090 or 4090 cards. Using Ollama or llama.cpp with 4-bit quantization will allow you to split the model across these cards. On Apple Silicon, an M2 or M3 Ultra with at least 128GB of Unified Memory is the ideal environment for this model, providing enough headroom for the 1M token context window.

On a dual A100 setup using FP8 quantization, expect high throughput (50+ tokens/sec) due to the MTP layers. On consumer 4090 clusters using GGUF, performance will likely settle between 5-15 tokens/sec depending on the interconnect speed (PCIe Gen4 vs. Gen5).

Nemotron 3 Super enters a competitive field of "large-but-efficient" models.

Qwen 3.5 122B is a dense model, meaning every parameter is active for every token. While Qwen may offer slightly higher raw knowledge density, Nemotron 3 Super is significantly faster in local inference. In NVIDIA's own benchmarks, Nemotron achieves up to 7.5x higher throughput in long-context scenarios compared to Qwen. If your application involves high-volume token generation (like autonomous agents), Nemotron is the clear winner.

DeepSeek-V3 is a much larger MoE (671B total parameters). While DeepSeek-V3 is arguably the current state-of-the-art for open-weights reasoning, its VRAM requirements are astronomical compared to Nemotron. Nemotron 3 Super provides "frontier-adjacent" reasoning while remaining small enough to fit on a single high-end workstation or a small Mac Studio, whereas DeepSeek-V3 requires a dedicated server rack.

Choose this model if you need a "reasoning backbone" for a local agentic system where speed and context length are more important than world-knowledge trivia. It is the most technically advanced hybrid model currently available for local deployment that can realistically handle a million-token context window without collapsing into gibberish.