Welcome to the NVIDIA Developer YouTube Channel Subscribe to this channel for easy-to-follow “how-to” videos to learn about the latest technologies for developers from NVIDIA. Whether you’re a student, professional developer, or tech enthusiast, discover: 🧑💻 CUDA Programming: Parallel computing, debugging, and performance tips ✨ Agentic & Generative AI: Build intelligent agents and generative apps with AgentIQ, NeMo, and open-source tools 🤖 Robotics: Unlock smart automation and robotics solutions 📊 Data Science & Analytics: Accelerate data workflows with GPU-powered libraries like RAPIDS and popular tools 🛠️ And More: Deep learning, computer vision, simulation, high-performance computing, SDK tutorials, and expert guides Join a vibrant developer community, stay ahead with emerging tech, get real-world examples, and tips from NVIDIA engineers. Subscribe and start creating, optimizing, and deploying innovations with NVIDIA. 🙌
NVIDIA researchers demonstrate training a multilingual code generation model from scratch using 43x less data than typical foundation models, achieving 38.87% accuracy on HumanEval+ while supporting English/Spanish and Python/Rust through efficient data curation and checkpoint merging.
Google's Jeff Dean and NVIDIA's Bill Dally discuss the rapid evolution toward autonomous AI agents capable of multi-day tasks and self-improvement, while detailing the radical hardware shifts—toward 'speed of light' latency and specialized inference chips—required to power this next frontier.
MLOps requires balancing scientific rigor with engineering discipline, combining rigorous hypothesis testing and data validation with robust system design, interface contracts, and continuous production monitoring to avoid catastrophic failures and pseudoscientific pitfalls.
Adobe's CTO explains why the company chose to build proprietary generative AI models from scratch to ensure legal compliance and creative control, then details how they discovered that naive scaling approaches resulted in GPUs sitting idle 60-70% of the time due to coordination bottlenecks.
NVIDIA is driving a paradigm shift from cloud-based LLMs to local small language models (SLMs) on RTX GPUs, enabling personalized agentic AI with full data privacy. Through advanced quantization and tools like Olama, developers can now run sophisticated coding agents and creative assistants entirely on local hardware with 11x performance gains over competitors.
NVIDIA Research reveals architectural breakthroughs targeting 16,000 tokens/sec inference speeds through radical data movement reduction, while recounting how the 500-person team previously pioneered the company's AI, networking, and ray tracing transformations.
Leading researchers and executives discuss how open source AI has evolved from a values-based movement into a viable commercial ecosystem, with companies like NVIDIA, Databricks, and Hugging Face demonstrating that open-weight models and transparent research can drive both industry innovation and sustainable business models through cloud services and foundation model programs.
NVIDIA Research unveils breakthroughs shifting AI from imitation to exploration through Reinforcement Learning as Pre-training (RLP), open-sources the Alpamayo reasoning platform for autonomous vehicles, and demonstrates real-time generative world models and neural physics simulators enabling zero-shot sim-to-real robotics transfer.
This presentation outlines CUDA's evolution toward 'guaranteed asymmetric parallelism,' introducing Green Contexts to enable dynamic GPU resource partitioning for disaggregated AI inference workloads, while previewing future multi-node CUDA graphs that will orchestrate computations across entire data centers.
This session traces the rapid evolution of AI from simple chatbots to autonomous 'agentic' systems capable of reasoning, coding new abilities, and collaborating in multi-agent networks, while demonstrating how developers can now build functional AI agents using modular tools and NVIDIA's open blueprints.
NVIDIA's Nemotron project represents a strategic shift toward open-source AI development, releasing not just large language models (Nano, Super, Ultra) but complete training datasets, algorithms, and techniques to accelerate the entire ecosystem while informing NVIDIA's future hardware designs.
NVIDIA Cosmos Reason 2 enables physical AI systems to interpret the physical world through structured reasoning and common sense. The session highlights Milestone Systems' deployment of fine-tuned models for smart city traffic analytics, achieving automated incident detection and reporting at city scale.