NVIDIA AI Podcast

NVIDIA AI Podcast

NVIDIA researchers Lynn Chai and Luc introduce TensorRT Edge LLM, a purpose-built inference engine for deploying large language models on Jetson edge devices, showcasing NVFP4 quantization and speculative decoding techniques that achieve up to 7x faster prefill speeds and 500 tokens per second generation while previewing a simplified vLLM-style Python API coming soon.

NVIDIA AI Podcast

This Jetson AI Lab Research Group call features lightning talks on open-source hardware for remote Jetson access, a real-time emotional AI engine for robots running entirely on Jetson Nano, and updates to the Jetson AI Lab model repository with new performance benchmarks and deployment guides.

NVIDIA AI Podcast

Cameron Rose presents 'Operation Squirrel,' an autonomous drone project using Jetson Orin Nano for real-time target tracking and dynamic payload delivery. The system uses a modular C++ software stack with TensorRT-optimized YOLO and OSNet running at 21 FPS, communicating via UART with a flight controller to maintain following distance through velocity commands.

NVIDIA AI Podcast

NVIDIA's Isaac ROS team explains how their NITROS framework eliminates costly GPU memory copies in ROS 2 to enable a new era of "Physical AI" where end-to-end learned policies replace traditional robotic control, requiring tight integration of accelerated computing from simulation to deployment on Jetson.

NVIDIA AI Podcast

NVIDIA's Aerial Framework enables 6G researchers to write radio access network algorithms in Python/JAX and compile them directly to GPU-accelerated TensorRT engines, eliminating the traditional rewrite-to-C++ bottleneck while meeting sub-500-microsecond real-time latency requirements for over-the-air testing.

NVIDIA AI Podcast

NVIDIA Aerial Framework eliminates the traditional bottleneck of manually converting 6G RAN research into production C++ code by automatically lowering Python, JAX, and PyTorch algorithms into real-time CUDA kernels with microsecond latency, enabling rapid over-the-air deployment cycles.

NVIDIA AI Podcast

NVIDIA Research introduces the Sionna Research Kit, an open-source, $6,000-$8,000 platform running on DGX Spark that bridges simulation and reality by enabling real-time prototyping of AI-native 6G networks with neural receivers, digital twin channel emulation, and commercial 5G hardware integration.

NVIDIA AI Podcast

As AI tools generate over one billion lines of code daily, organizations face a critical bottleneck where manual code reviews cannot scale to catch the 41% increase in bugs, necessitating AI-driven review systems powered by sophisticated context engineering rather than traditional prompt engineering.

NVIDIA AI Podcast

UT Austin researchers demonstrate a real-time tsunami forecasting system using physics-based digital twins and Bayesian inversion, winning the Gordon Bell Prize by reducing computational time for billion-parameter inverse problems from decades to milliseconds through novel GPU-accelerated algorithms.

NVIDIA AI Podcast

Cursor co-founder Aman traces AI coding's evolution from autocomplete to synchronous agents, outlining the shift toward long-running async cloud agents that use multi-agent architectures to overcome context limits, and predicting a future of self-driving codebases with self-healing systems and minimal human intervention.

NVIDIA AI Podcast

Industry leaders from NVIDIA, Hugging Face, Mistral AI, Black Forest Labs, and Lightricks discuss how open-source inference optimization—spanning quantization, latent compression, and Mixture of Experts architectures—is enabling both massive trillion-parameter models and efficient edge deployment while driving the shift toward sovereign AI and local data control.

NVIDIA AI Podcast

Former OpenAI researchers now leading frontier startups explain how reinforcement learning has evolved from game-playing agents to powering enterprise automation and scientific discovery, requiring new scaling paradigms focused on inference compute and long-horizon reasoning rather than just pre-training FLOPs.