Generative AI

Artificial intelligence and accelerated computing are being used to help solve the world’s greatest challenges. NVIDIA has reinvented the computing stack — spanning GPUs, CPUs, DPUs, networking and software. Our platform drives the AI revolution, powering hundreds of millions of devices in every cloud and fueling 75% of the world’s TOP500 supercomputers. Put in the Read Article

The pace of technology innovation has accelerated in the past year, most dramatically in AI. And in 2024, there was no better place to be a part of creating those breakthroughs than NVIDIA Research. NVIDIA Research is comprised of hundreds of extremely bright people pushing the frontiers of knowledge, not just in AI, but across Read Article

NVIDIA’s AI Podcast gives listeners the inside scoop on the ways AI is transforming nearly every industry.  Since the show’s debut in 2016, it’s garnered more than 6 million listens across 200-plus episodes, covering how generative AI is used to power applications including assistive technology for the visually impaired, wildfire alert systems and the Roblox Read Article

Optimizing costs of generative AI applications on AWS is critical for realizing the full potential of this transformative technology. The post outlines key cost optimization pillars, including model selection and customization, token usage, inference pricing plans, and vector database considerations.

In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. We use HuggingFace’s Optimum-Neuron software development kit (SDK) to apply LoRA to fine-tuning jobs, and use SageMaker HyperPod as the primary compute cluster to perform distributed training on Trainium. Using LoRA supervised fine-tuning for Meta Llama 3 models, you can further reduce your cost to fine tune models by up to 50% and reduce the training time by 70%.

The AI Decoded series over the past year has broken down all things AI — from simplifying the complexities of large language models (LLMs) to highlighting the power of RTX AI PCs and workstations.