Nemotron 3 Super
NVIDIA Nemotron 3 Super. 120B open MoE activating just 12B parameters. Delivers maximum compute efficiency and accuracy for complex multi-agent applications.
Best for maximum compute efficiency for multi-agent workflows
Sizes 120B (12B active MoE)
Context 128K
License NVIDIA Open Model License
Min VRAM (default size, Q4) 48 GB
Rec VRAM 96 GB
What is Nemotron 3 Super?
Nemotron 3 Super is NVIDIA's MoE open-weights language model. Released in 2026-03, it ships in 1 size (120B (12B active MoE)) and is licensed NVIDIA Open Model License. The most popular use case is maximum compute efficiency for multi-agent workflows.
VRAM and hardware
The 120B (12B active MoE) size fits in 48 GB of VRAM.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| 120B (12B active MoE) | 48 GB | 72 GB | ~14 tok/s |
How to run Nemotron 3 Super locally
Option 1: Ollama (simplest)
ollama pull nemotron-3-super
ollama run nemotron-3-super Option 2: Mullama (production)
mullama pull nemotron-3-super
mullama run nemotron-3-super Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "nemotron-3-super gguf")
./llama-cli -m nemotron-3-super.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("nemotron-3-super.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Nemotron 3 Super
- multi-agent
- agentic
- reasoning
- tool use
Hardware it fits on
- Apple Silicon (8GB): 120B (12B active MoE) in MLX
- Apple Silicon (16GB): 120B (12B active MoE) in MLX
- Apple Silicon (32GB): 120B (12B active MoE) in MLX or GGUF
- Apple Silicon (64GB+): 120B (12B active MoE) in MLX
- RTX 3090 (24GB): 120B (12B active MoE) at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): 120B (12B active MoE) at Q8_0
- 2× RTX 6000 Ada (96GB total): 120B (12B active MoE) at Q4_K_M
Related models in the Nemotron family
This is the only Nemotron-family model in the catalog right now.
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware