What Nemotron 3 Ultra delivers
Nemotron 3 Ultra scores 71.9% on SWE-bench Verified, 87% on MMLU-Pro, and achieves the highest Artificial Analysis Intelligence Index (48) among US-developed open-weight models. It maintains 91.75% on RULER at 1M tokens — far outperforming competitors on long-context retention.
At 300+ tokens per second on commodity US GPU infrastructure, it is also one of the fastest models for its capability class.
The Nemotron 3 family
Nano: Smallest variant for edge and consumer GPU deployment.
Super: Mid-range with strong long-context retention (91.75% at 1M tokens on RULER).
Ultra: Flagship with 71.9% SWE-bench Verified, best for enterprise coding and reasoning.
Deployment options
Self-host: Download weights from Hugging Face. Optimized for NVIDIA GPUs with TensorRT-LLM.
API: Free on OpenRouter with pay-per-token pricing from partners.
Cloud: Available on major cloud GPU providers with pre-configured NVIDIA NIM containers.
Best for US enterprise deployments
Data residency requirements make Nemotron 3 Ultra the safest choice for US-based organizations that cannot use Chinese-origin model weights.
For pure intelligence score without compliance constraints, Kimi K2.6 and GLM 5.2 score higher overall.
Ultra's throughput advantage (300+ tok/s) makes it ideal for high-volume agent pipelines.
Training infrastructure is also open
NVIDIA released the entire training infrastructure: NeMo Data Designer for synthetic data, the RL training pipeline, and the NeMo ecosystem tools.
This makes Nemotron 3 one of the most reproducible open-weight model releases.
FAQ
Can Nemotron 3 Ultra run on consumer hardware?
Ultra requires multi-GPU setups. The Nano variant is designed for consumer GPUs and edge deployment.
What license does Nemotron 3 use?
NVIDIA's custom open-weight license. Review terms for commercial use restrictions on the Hugging Face model card.
Deploy Nemotron 3 Ultra
- Choose variant: Ultra for flagship, Super for mid-range, Nano for edge
- Download weights from Hugging Face
- Serve with TensorRT-LLM for maximum throughput on NVIDIA GPUs
- Configure with 1M context for long-document workloads
- Deploy via NIM container for production
- Benchmark throughput against your latency requirements
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