Nemotron 3.5 Lightning Operational

Nemotron 3.5 Lightning is an open-source 30B-parameter MoE model developed by NVIDIA, specifically optimized for multi-agent systems. Compared with similar models, it delivers up to 4× faster output and accelerates agent task completion by 30%, achieving an excellent balance between accuracy and speed. The model supports deployment across the full range of environments, from RTX PCs and Jetson devices to data centers. It is open source on platforms including Hugging Face and NVIDIA Build, helping developers create more efficient agent applications.

Multi-agent task execution: The model is specifically designed for targeted tasks in large-scale multi-agent systems, helping developers build smarter and more efficient agent applications.
High-speed inference and output: Compared with similar models, Nemotron 3.5 Lightning delivers up to 4× faster output and accelerates overall agent task completion by 30%.
Flexible deployment across environments: The model can run directly on local AI systems, including NVIDIA RTX PCs, DGX Spark, and Jetson devices, and can seamlessly scale to edge devices, workstations, data centers, and cloud environments.

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MoE mixture-of-experts architecture: Nemotron 3.5 Lightning uses a 30B-parameter MoE architecture, with a total of 30 billion parameters. Each inference activates only a subnetwork of approximately 3 billion parameters. Through sparse activation, it substantially reduces computational overhead while preserving the capabilities of a large model, enabling efficient inference.
NVFP4 quantization: The model supports the NVFP4 quantization format, NVIDIA's proprietary 4-bit floating-point precision scheme. It significantly compresses model size and memory usage, allowing smooth operation on consumer and edge devices such as RTX PCs and Jetson systems.
Specialized optimization for agent scenarios: The model is specifically trained and optimized for multi-agent collaboration scenarios, with a focus on improving the stability and accuracy of long-chain reasoning, tool use, and task decomposition to better support complex agent workflows.
Joint speed-and-accuracy optimization: Through its architecture and training strategy, Nemotron 3.5 Lightning jointly optimizes accuracy and inference speed. It demonstrates stronger overall performance than similar models in the PinchBench benchmark, maintaining task quality while increasing overall agent execution speed by 30%.

Hugging Face download: Visit the official Hugging Face repository to download the model weights and load Nemotron 3.5 Lightning locally.
NVIDIA Build platform: Visit the official NVIDIA Build website for model details, technical documentation, and deployment guides.
Local device deployment: Deploy the model directly on local devices such as NVIDIA RTX PCs, DGX Spark, DGX Station, or Jetson systems.
Enterprise scaling: Seamlessly scale to RTX PRO workstations, data centers, or cloud environments to meet enterprise application needs.

MoE sparse activation architecture: The model uses an MoE architecture with 30B total parameters and approximately 3B activated parameters, substantially reducing computational overhead while preserving large-model capabilities.
Ultra-fast inference: It delivers 4× faster output than similar models and can accelerate overall agent task completion by 30%.
NVFP4 quantization support: NVIDIA's proprietary 4-bit floating-point quantization format significantly reduces memory usage, enabling deployment across environments from RTX PCs to data centers.
Specialized agent optimization: The model is specifically trained for long-running multi-agent tasks, achieving an excellent balance between accuracy and inference speed.

Project website: https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/
Hugging Face model repository: https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4

Multi-agent collaboration systems: As a specialized task model, it drives complex Agent workflows and efficiently handles tool use, task decomposition, and long-chain reasoning.
Local edge AI inference: Leveraging NVFP4 quantization and sparse activation, it enables low-latency, low-power local inference on RTX PCs and Jetson devices.
Enterprise agent platforms: It seamlessly scales to data centers and cloud environments to support high-frequency, long-running enterprise agent services and automated workflows.
Code development and DevOps automation: It rapidly 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 responses, it provides fast and accurate inference results to support real-time decision-making.