On June 1, 2026, at the Computex conference in Taipei, NVIDIA CEO Jen-Hsun Huang releasedNemotron 3 Ultra——A 550 billion parameter (550B) open weight mixed expert (MoE) model, positioned as "born for long-running AI agents."The most powerful open source AI model in the United States.
core competencies
Nemotron 3 Ultra adopts a 90% sparse MoE architecture, which only activates about 55B parameters out of 550B for each inference, significantly reducing computing costs while ensuring inference quality.Agent workflow——Including planning, reasoning, tool invocation, code writing and debugging, research assistance, and long-term tasks across steps.
- total parameters: 550B, each inference activation is about 55B
- Architecture: Mixed Expert Model (MoE), 64 experts, 8 per activation
- context window:Support long sequence reasoning
- License Agreement：Linux Foundation OpenMDW open source protocol, weights, data, and training recipes are all open
- Deployment method:Supports cloud, local and edge deployment
User experience/limitations
The advantages of Nemotron 3 Ultra are obvious: completely open, optimized for agents, and highly efficient in reasoning.Claude There is still a clear gap between closed source flagships such as Opus 4.8 (60+ points) and GPT-5.5.
Nemotron 3 Ultra vs. Other Open-Source Models: Side-by-Side Comparison
The open-source model competition will be fierce in 2026, and Nemotron 3 Ultra’s positioning must be viewed within this framework:
| Model | Parameter count | Core Positioning | This Site’s Rating |
|---|---|---|---|
| NVIDIA Nemotron 3 Ultra | 550B (90% sparse MoE) | Agent-optimized, U.S. open-source flagship | 6.8 |
| GLM-5.2 | ~753B | Programming and Agent capabilities, top overall open-source model | 8.6 |
| DeepSeek V4 Pro | MoE architecture | 1M context window, flagship performance at consumer-grade pricing | 8.5 |
| Qwen3.8 2.4T A95B | 2.4 trillion | Ultra-large-scale open-source weights | 8.3 |
| MiniMax M3 | Million-token open weights | Programming model, leading on SWE-Bench Pro | 8.1 |
It is evident that Nemotron 3 Ultradoes not lag in parameter count, yet our site’s benchmarking yields a composite score of 6.8—lower than contemporary domestic open-source models. The gap stems primarily from Chinese-language support and ecosystem maturity, not foundational capability. For a detailed comparison, see GLM-5.2 in-depth review and DeepSeek V4 Pro In-Depth Evaluation.
Deployment requirements and applicable use cases
Hardware requirements: practical accessibility for individual users
Even with 90% sparse MoE, the full 550B-parameter model still demands multi-GPU, data-center-class VRAM for deployment (see comparable-scale models like Qwen3.8 2.4T Deployment requirements). After quantization, it can run on high-end multi-GPU workstations, butIt is essentially infeasible on a single personal machine.—This is one of the main reasons this site awarded it a score of 6.8 instead of higher.
Two truly suitable user groups
- Enterprises with self-built inference clusters: Value fully open weights and customizability, and need to avoid vendor lock-in from API providers
- Long-running agent systems: Nemotron 3 Ultra is specifically optimized for agent scenarios; stability in long-duration tasks is one of its key strengths (for reference, see Xiaomi MiMo Code ’s agent long-task performance)
Less Suitable Scenarios
Chinese content creation and Chinese technical documentation processing remain domestic models’ home turf (see GLM 5.3 and Kimi K2.7 Code). If your primary workload is Chinese-language, Nemotron 3 Ultra’s cost-effectiveness is not outstanding.
Overall Score
| 维度 | Score | evaluate |
|---|---|---|
| functional completeness | 7.5 / 10 | The agent workflow has comprehensive coverage, but lacks multi-modal capabilities and consumer-level scene optimization |
| 易用性 | 6.0 / 10 | The deployment threshold is extremely high (multi-card cluster required), and ordinary users cannot use it directly. |
| Cost-effectiveness | 8.0 / 10 | Completely open source and free, MoE architecture inference cost is low, commercial license is friendly |
| 中文支持 | 5.0 / 10 | There is currently no Chinese benchmark test data. The training data is mainly in English, and the Chinese ability is questionable. |
| 输出质量 | 7.5 / 10 | The AI² index is 48 points. The United States has the strongest open source, but there is still a gap between it and the closed source flagship. |
Overall rating: 6.8/10
Frequently Asked Questions (FAQ)
Can NVIDIA Nemotron 3 Ultra be used commercially for free?
It adopts a fully open-weight license (NVIDIA Open Model License), permitting commercial use and derivative development without usage fees. This is its most fundamental distinction from closed-source flagship models—enterprises can self-deploy, fine-tune, and privatize it without paying per-API-call fees.
What hardware configuration is required to run it?
Its 550B total parameters (90% sparse MoE, ~55B activated per inference) require multi-GPU data-center-level VRAM. After quantization, it can run on high-end multi-GPU workstations, but remains essentially infeasible on personal single-machine devices. Individual developers are advised to invoke it on-demand via hosted platforms.
Nemotron 3 Ultra vs. GLM-5.2: Which should you choose?
It depends on your use case:Need open weights, English agent tasks, or a self-built cluster → Nemotron 3 Ultra；Requires programming capability, Chinese-language support, and lower usage cost → GLM-5.2(This site’s rating: 8.6 vs. 6.8—the gap stems mainly from Chinese-language performance and ecosystem maturity.)
Does it support Chinese? How well does it perform?
Yes, but it is not a strength. As a model developed by a U.S. lab, its Chinese understanding and Chinese technical content generation capabilities are clearly weaker than those of contemporary domestic models (GLM, Qwen,DeepSeek,Kimi series). For Chinese-dominant scenarios, domestic open-source models are recommended as the top choice.
What does “90% sparsity MoE” mean?
The model has 550B total parameters, but only ~55B (10%) are activated per inference. This preserves the knowledge capacity of an ultra-large model while reducing per-inference computational cost to levels comparable to mid-sized models—a mainstream architectural choice for lowering inference costs in today’s LLMs.DeepSeek GLM series adopts a similar approach.
NVIDIA Nemotron 3 Ultra is an important milestone for the open source AI community - it proves that American laboratories can also produce competitive open weight models.
View our AI tools vs. decision engines, learn more about AI model reviews and recommendations.
Want to Discover More Useful AI Tools? Explore Our AI Model Library and Tool Comparison Engineor continue reading:NVIDIA Cosmos 3 benchmark · GLM-5.2 benchmark · DeepSeek V4 Pro benchmark.