What Is Nemotron 3.5 Lightning
Nemotron 3.5 Lightning is a 30B-parameter open-source MoE model from NVIDIA, optimized for multi-agent systems. Compared with similar models, it delivers 4x faster output and completes agent tasks 30% faster, achieving an excellent balance between accuracy and speed. The model supports deployment across all scenarios, from RTX PCs and Jetson to data centers, and has been open-sourced on platforms such as Hugging Face and NVIDIA Build, helping developers build more efficient agent applications.
Key Features of Nemotron 3.5 Lightning
- Multi-Agent Task Execution: The model is purpose-built for specific tasks within large multi-agent systems, helping developers build smarter, more efficient agent applications.
- High-Speed Inference Output: Compared with similar models, Nemotron 3.5 Lightning can achieve up to 4x faster output and speeds up overall agent task completion by 30%.
- Flexible Deployment Across All Scenarios: The model can run directly on local AI systems (including NVIDIA RTX PCs, DGX Spark, Jetson, etc.) and can also scale seamlessly to edge devices, workstations, data centers, and cloud environments.
Technical Principles of Nemotron 3.5 Lightning
- MoE Mixture-of-Experts Architecture: Nemotron 3.5 Lightning uses a 30B-parameter MoE (mixture-of-experts) architecture, with a total parameter count of 30 billion and only about 3 billion parameters activated per inference. Through sparse activation, it greatly reduces computational overhead while maintaining large-model capabilities, enabling efficient inference.
- NVFP4 Quantization Compression: The model supports the NVFP4 quantization format, NVIDIA's in-house 4-bit floating-point precision scheme, which significantly compresses model size and memory usage, allowing it to run smoothly on consumer and edge devices such as RTX PCs and Jetson.
- Specialized Optimization for Agent Scenarios: The model is specially trained and optimized for multi-agent collaboration scenarios, with a focus on improving the stability and accuracy of key steps such as long-chain reasoning, tool calling, and task decomposition, better supporting complex agent workflows.
- Joint Speed-Accuracy Optimization: In architecture design and training strategy, Nemotron 3.5 Lightning achieves joint optimization of accuracy and inference speed. It demonstrates overall performance superior to similar models on the PinchBench benchmark, ensuring task completion quality while increasing overall agent execution speed by 30%.
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How to Use Nemotron 3.5 Lightning
- Hugging Face Download: Visit the official Hugging Face repository to download the model weights and load and run Nemotron 3.5 Lightning locally.
- NVIDIA Build Platform: Go to the NVIDIA Build website to access the model introduction, technical documentation, and deployment guide.
- Local Device Deployment: Deploy the model directly on local devices such as NVIDIA RTX PCs, DGX Spark, DGX Station, or Jetson.
- Enterprise-Grade Scaling: Seamlessly scale to RTX PRO workstations, data centers, or cloud environments to meet enterprise application needs.
Core Advantages of Nemotron 3.5 Lightning
- MoE Sparse Activation Architecture: The model adopts an MoE architecture with 30B total parameters and about 3B activated parameters, greatly reducing computational overhead while maintaining large model capabilities.
- Extremely fast inference performance: Compared with similar models, output speed is increased by 4 times, and the overall completion speed of agent tasks can be accelerated by 30%.
- NVFP4 quantization support: Through NVIDIA's self-developed 4-bit floating-point quantization format, VRAM usage is significantly compressed, enabling deployment across all scenarios from RTX PCs to data centers.
- Specialized optimization for agents: Specially trained for long-duration multi-agent tasks, achieving an excellent balance between accuracy and inference speed.
Project address of Nemotron 3.5 Lightning
- Project official website：https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/
- HuggingFace model repository：https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4
Comparison of Nemotron 3.5 Lightning with similar competitors
| Comparison dimension | Nemotron 3.5 Lightning | Qwen3.6-35B-A3B |
|---|---|---|
| Publisher | NVIDIA | Alibaba Tongyi Qianwen |
| Total parameter count | 30B | 35B |
| Activated parameter count | About 3B (30B-A3B) | 3B |
| Architecture | MoE (Mixture of Experts) | MoE (Mixture of Experts) |
| Context length | Not explicitly mentioned | 200K |
| Open-source license | Fully open source | Apache 2.0 (commercially usable) |
| Inference speed | Output speed increased by 4 times, task completion accelerated by 30% | Efficient inference, specific multiplier not publicly disclosed |
| Programming capability | Specific benchmark not explicitly stated | SWE-bench Verified 73.4，Terminal-Bench 2.0 51.5 |
| Multimodal support | Not explicit | Native support, visual capability on par with Claude Sonnet 4.5 |
| Reasoning mode | Not explicit | Supports flexible switching between thinking/non-thinking modes |
Application scenarios of Nemotron 3.5 Lightning
- Multi-agent collaboration systems: As a specialized task model, it drives complex Agent workflows and efficiently completes tool calls, task decomposition, and long-chain reasoning.
- Local edge AI inference: Relying on NVFP4 quantization and sparse activation mechanisms, it achieves low-latency, low-power local inference on RTX PCs and Jetson devices.
- Enterprise-grade agent platforms: Seamlessly scales to data centers and the cloud, supporting high-frequency, long-running enterprise-grade agent services and automated processes.
- Code development and DevOps automation: Quickly generates, reviews, and debugs code, accelerating agent task execution in software development and continuous integration.
- Real-time data analysis and decision support: In business scenarios requiring rapid response, it provides fast and accurate inference results to assist real-time decision-making.