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Automotive
AI Models for In-Vehicle Applications
Leverage AI models to improve embedded software for automotive applications, boosting efficiency and accelerating deployment.
nvidiasparsedrive
End-to-end autonomous driving stack integrating perception, prediction, and planning with sparse scene representations for efficiency and safety.
Enterprise AI Models for Cloud Deployment
Leverage advanced AI models to streamline automotive software development and optimize cloud deployment.
nvidiacosmos-transfer1-7b
Generates physics-aware video world states for physical AI development using text prompts and multiple spatial control inputs derived from real-world data or simulation.
nvidiacosmos-predict1-7b
Generalist model to generate future world state as videos from text and image prompts to create synthetic training data for robots and autonomous vehicles.
nvidiacosmos-predict1-5b
Generates future frames of a physics-aware world state based on simply an image or short video prompt for physical AI development.
metallama-3.3-70b-instruct
Advanced LLM for reasoning, math, general knowledge, and function calling
metallama-3.1-405b-instruct
Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks.
qwenqwen2.5-7b-instruct
Chinese and English LLM targeting for language, coding, mathematics, reasoning, etc.
deepseek-aideepseek-r1
State-of-the-art, high-efficiency LLM excelling in reasoning, math, and coding.
Explore NVIDIA Automotive Blueprints
Comprehensive reference workflows that accelerate automotive software development with NVIDIA acceleration libraries, APIs, NIM, and microservices.
nvidiaBuild an Enterprise RAG pipeline
Continuously extract, embed, and index multimodal data for fast, accurate semantic search. Built on world-class NeMo Retriever models, the RAG blueprint connects AI applications to multimodal enterprise data wherever it resides.