NVIDIA Nemotron
NVIDIA Nemotron is a family of open-source models developed by NVIDIA, designed to generate synthetic data for training large language models (LLMs) for commercial applications. The Nemotron-4 340B model, in particular, is a significant release by NVIDIA, offering developers a powerful tool to generate high-quality data and filter it based on various attributes using a reward model.
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Nemotron 3 Ultra
Nemotron 3 Nano is a compact, open large language model in NVIDIA’s Nemotron 3 family, designed for efficient agentic reasoning, conversational AI, and coding tasks. It uses a hybrid Mixture-of-Experts Mamba-Transformer architecture that activates only a small subset of parameters per token, enabling low-latency inference while maintaining strong accuracy and reasoning performance. It has approximately 31.6 billion total parameters with around 3.2 billion active (3.6 billion including embeddings), allowing it to achieve higher accuracy than previous Nemotron 2 Nano while using less computation per forward pass. Nemotron 3 Nano supports long-context processing of up to one million tokens, enabling it to handle large documents, multi-step workflows, and extended reasoning chains in a single pass. It is designed for high-throughput, real-time execution, excelling in multi-turn conversations, tool calling, and agent-based workflows where tasks require planning, reasoning, and more.
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Nemotron 3
NVIDIA Nemotron 3 is a family of open large language models developed by NVIDIA to power advanced reasoning, conversational AI, and autonomous AI agents. The Nemotron 3 series includes three models designed for different scales of AI workloads while maintaining high efficiency and accuracy. These models focus on “agentic AI” capabilities, meaning they can perform multi-step reasoning, coordinate with tools, and operate as components within multi-agent systems used in automation, research, and enterprise applications. The architecture uses a hybrid mixture-of-experts (MoE) design combined with transformer-based techniques, allowing the model to activate only a subset of parameters for each task, which improves performance while reducing computational cost. Nemotron 3 models are built to deliver strong reasoning, conversational, and planning abilities while maintaining high throughput for large-scale deployment.
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Nemotron 3.5 Lightning
NVIDIA Nemotron 3.5 Lightning is an open 30B-parameter mixture-of-experts model with 3B active parameters, designed for high-volume, low-latency execution in long-running and always-on AI agents. Built for the execution layer of agentic systems, it handles frequent tasks such as tool calls, output validation, routine commands, and subagent delegation while larger reasoning models focus on planning and orchestration. Its MoE architecture activates only a fraction of parameters for each token, combining the capacity of a larger model with lower compute requirements. The model is trained for popular agent harnesses and supports speculative decoding through multi-token prediction, DFlash, and DSpark to improve inference speed across different serving scenarios. It is available with BF16 and NVFP4 checkpoints and can run from local systems such as DGX Spark and GeForce RTX hardware to data center environments.
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