NVIDIA's NIM API catalog offers Nemotron and Llama-3.1 models from compact edge variants to ultra-scale versions, supporting language, vision-language, coding, math, and specialized tasks. The diverse parameter sizes and modalities let developers balance accuracy with efficiency across PCs, on-device inference, and high-performance servers. Key Points: - Models range from 4B "nano" to 253B "ultra" parameters for flexible accuracy-efficiency tradeoffs - Multi-modal options combine text, image, and video understanding - Advanced reasoning, coding, and math capabilities for scientific and AI agent use cases - Edge-optimized variants enable low-latency, on-device inference - Bilingual Hindi-English model expands language support - 70B reward model facilitates RLHF for better human alignment - Emphasis on high inference efficiency and domain versatility https://build.nvidia.com/search/models?filters=publisher%3Anvidia&q=Nemotron&ncid=no-ncid

NVIDIA's Nemotron family delivers enterprise-grade multimodal AI models designed for complex reasoning tasks across scientific research, advanced mathematics, coding, and visual analysis. The lineup includes three variants optimized for different deployment scenarios: Nano for edge computing and cost-sensitive applications, Super for single-GPU workloads balancing performance and efficiency, and Ultra for maximum accuracy in data center environments. Unlike many AI models with restrictive licensing, Nemotron offers commercial viability with an open license that allows organizations to customize the models while maintaining control over their data and deployments. #Nemotron #NVIDIANemotron #NVIDIA #MultimodalAI #EnterpriseAI #AICoding #AIScience #AIReasoning #OpenSourceAI #EdgeAI #ComputerVision #AIModels #MachineLearning #ArtificialIntelligence #TechInnovation https://build.nvidia.com/nvidia/llama-3_1-nemotron-ultra-253b-v1 https://github.com/NVIDIA/GenerativeAIExamples