Holo3

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.

H Company’s Holo2 model family delivers cost-efficient, high-performance vision-language models tailored for computer-use agents that navigate, localize UI elements, and act across web, desktop, and mobile environments. The series, available in 4 B, 8 B, and 30 B-A3B sizes, builds on their earlier Holo1 and Holo1.5 models, retaining strong UI grounding while significantly enhancing navigation capabilities. Holo2 models use a mixture-of-experts (MoE) architecture, activating only necessary parameters, to optimize efficiency. Trained on curated localization and agent datasets, they can be deployed as drop-in replacements for their predecessors. They support seamless inference in frameworks compatible with Qwen3-VL models and can be integrated into agentic pipelines like Surfer 2. In benchmark testing, Holo2-30B-A3B achieved 66.1% accuracy on ScreenSpot-Pro and 76.1% on OSWorld-G, leading the UI localization category.