Decoding the Enigma: Unraveling the Force Behind AI’s Hardware, Software, and Tools

In 2018, nVidia launched RTX technologies and introduced the first consumer GPU built for AI, the Geforce RTX. This marked a significant shift towards AI computing, and since then, AI on RTX PCs and workstations has flourished into a thriving ecosystem with over 100 million users and 500 AI applications. With the emergence of generative AI, a new wave of capabilities is being ushered in from PC to cloud, and NVIDIA’s expertise in AI ensures that users have the performance they need to handle a wide range of AI features.

AI on RTX has already made an impact on both productivity and entertainment. Home and office users are taking advantage of AI on RTX to enhance their software, resulting in higher frame rates and stunning resolutions for gamers, streamlined workflows for developers using generative AI, and the ability for creators to focus on their creativity rather than mundane tasks. As AI research advances, it will continue to tackle more complex tasks, and the demanding performance needs will be met by RTX.

But what exactly is AI? At its core, artificial intelligence is a smarter type of computing that allows computers or machines to think, learn, and take actions without explicit commands or constant user control. AI systems can perform tasks autonomously by analyzing vast amounts of data and recognizing patterns within it. This ability to learn from experiences or patterns allows AI systems to accomplish a wide variety of tasks, from image and speech recognition to language translation and medical diagnostics.

The next step in AI’s evolution is generative AI, which enables users to quickly create new content based on various inputs such as text, images, sounds, or 3D models. This content can be generated in the same form or transformed into something entirely new. Applications like ChatGPT and Stable Diffusion demonstrate the power of generative AI by generating long-form copy based on short text requests or turning descriptive text inputs into desired images.

To fully harness the power of AI, dedicated hardware is required. AI PCs, equipped with RTX GPUs, are designed to accelerate AI performance. These PCs feature specialized AI accelerators called Tensor Cores, which significantly speed up AI performance across demanding applications. The performance of AI systems is often measured in teraops (trillion operations per second), and the current generation of GeForce RTX GPUs offers a wide range of options, from roughly 200 AI TOPS to over 1,300 TOPS.

Having a PC capable of performing AI tasks locally offers several benefits, including availability without an internet connection, low Latency for high responsiveness, and increased privacy. More and more AI applications will require local computing capabilities, and RTX AI PCs are well-equipped to handle these tasks.

RTX GPUs not only provide performance but also introduce unique capabilities made possible by RTX technology. These AI features are accessible to millions of users, regardless of their skill level. RTX Video uses AI to upscale streaming video and display it in HDR, NVIDIA Broadcast improves video conferencing and livestreaming with AI-powered noise removal and background replacement, and Chat with RTX enables personalized AI chatbot interactions on local devices.

AI has had a significant impact on gaming performance over the past six years. NVIDIA DLSS, which uses AI to generate pixels in video games automatically, has boosted frame rates and improved image quality. With ongoing improvements, DLSS can now increase frame rates by up to 4x. Ray Reconstruction in the latest version of DLSS further enhances visual quality, setting a new standard for visually rich and immersive gameplay. Over 500 games and applications have embraced Ray Tracing, DLSS, and other AI-powered technologies to revolutionize the gaming experience.

AI is also transforming the creative process for artists and content creators by automating tedious tasks and freeing up time for pure creativity. Adobe Premiere Pro’s Enhance Speech tool uses AI to remove unwanted noise and improve the quality of dialogue clips, while Auto Reframe intelligently reframes video content for different aspect ratios. DaVinci Resolve’s Magic Mask simplifies the process of adjusting color and removing unwanted objects in videos. These AI features run fastest or exclusively on PCs with NVIDIA RTX or GeForce RTX GPUs.

Developers are benefiting from AI advancements as well. NVIDIA AI Workbench allows developers to create, test, and customize pretrained generative AI models using PC-class performance. TensorRT, a software that optimizes AI models, helps developers take full advantage of Tensor Cores in RTX GPUs. Text-based applications can now benefit from TensorRT acceleration with TensorRT-LLM for Windows, which improves LLM performance and includes pre-optimized checkpoints for popular models. Developers also have access to a TensorRT-LLM wrapper for the OpenAI Chat API, allowing for fast local LLM inference on RTX PCs.

The AI revolution is just beginning, and every week, we will explore the latest hardware, software, tools, and accelerations for RTX AI PC users. Welcome to AI Decoded, where we demystify AI and make this technology more accessible to all.

Background Information

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About Adobe:

Adobe is a software company for its creative software suites, including Photoshop, Illustrator, and Adobe Acrobat. They are widely used by professionals in the design, photography, and publishing industries, and their software plays a crucial role in digital content creation and management.

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About nVidia:

NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.

  

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Technology Explained

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DLSS: DLSS (Deep Learning Super Sampling) is an advanced AI-powered technology developed by NVIDIA that enhances real-time graphics rendering in video games and applications. DLSS utilizes deep learning algorithms to upscale lower-resolution images in real-time, resulting in higher-quality visuals while maintaining optimal performance. By harnessing the power of AI and deep neural networks, DLSS effectively boosts frame rates and image quality, enabling gamers to experience smoother gameplay and more immersive graphics without sacrificing computational efficiency. This technology has gained widespread recognition for its ability to deliver impressive visual fidelity and improved performance simultaneously, revolutionizing the way modern computer graphics are processed and displayed.

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Geforce: Geforce is a line of graphics processing units (GPUs) developed by Nvidia. It is the most popular GPU used in the computer industry today. Geforce GPUs are used in gaming PCs, workstations, and high-end laptops. They are also used in virtual reality systems, artificial intelligence, and deep learning applications. Geforce GPUs are designed to deliver high performance and power efficiency, making them ideal for gaming and other demanding applications. They are also capable of rendering high-resolution graphics and providing smooth, realistic visuals. Geforce GPUs are used in a variety of applications, from gaming to professional workstations, and are the preferred choice for many computer users.

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GPU: GPU stands for Graphics Processing Unit and is a specialized type of processor designed to handle graphics-intensive tasks. It is used in the computer industry to render images, videos, and 3D graphics. GPUs are used in gaming consoles, PCs, and mobile devices to provide a smooth and immersive gaming experience. They are also used in the medical field to create 3D models of organs and tissues, and in the automotive industry to create virtual prototypes of cars. GPUs are also used in the field of artificial intelligence to process large amounts of data and create complex models. GPUs are becoming increasingly important in the computer industry as they are able to process large amounts of data quickly and efficiently.

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HDR: High Dynamic Range (HDR) is a technology used to create images and videos with a greater range of luminance levels than what is possible with standard digital imaging. It allows for a greater range of colors and details to be captured in an image, resulting in a more realistic and vibrant look.

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Latency: Technology latency is the time it takes for a computer system to respond to a request. It is an important factor in the performance of computer systems, as it affects the speed and efficiency of data processing. In the computer industry, latency is a major factor in the performance of computer networks, storage systems, and other computer systems. Low latency is essential for applications that require fast response times, such as online gaming, streaming media, and real-time data processing. High latency can cause delays in data processing, resulting in slow response times and poor performance. To reduce latency, computer systems use various techniques such as caching, load balancing, and parallel processing. By reducing latency, computer systems can provide faster response times and improved performance.

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LLM: A Large Language Model (LLM) is a highly advanced artificial intelligence system, often based on complex architectures like GPT-3.5, designed to comprehend and produce human-like text on a massive scale. LLMs possess exceptional capabilities in various natural language understanding and generation tasks, including answering questions, generating creative content, and delivering context-aware responses to textual inputs. These models undergo extensive training on vast datasets to grasp the nuances of language, making them invaluable tools for applications like chatbots, content generation, and language translation.

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Ray Tracing: Ray tracing is an advanced rendering technique used in computer graphics to simulate the way light interacts with objects in a virtual environment. It traces the path of light rays as they travel through a scene, calculating how they interact with surfaces, materials, and lighting sources. By simulating the complex behaviors of light, ray tracing produces highly realistic and accurate visual effects, including lifelike reflections, shadows, and refractions. This technology enhances the overall visual quality of images and animations by accurately replicating how light behaves in the real world, resulting in a more immersive and visually stunning digital experience.

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Stable Diffusion: Stable Diffusion is a technology that is used to improve the performance of computer systems. It is a process of spreading out the load of a system across multiple processors or cores. This helps to reduce the amount of time it takes for a system to complete a task, as well as reduce the amount of energy used. Stable Diffusion is used in many areas of the computer industry, such as in cloud computing, distributed computing, and high-performance computing. It is also used in gaming, where it can help to reduce the amount of time it takes for a game to load. Stable Diffusion is also used in artificial intelligence, where it can help to improve the accuracy of machine learning algorithms.

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Tensor Cores: Tensor Cores are a type of specialized hardware designed to accelerate deep learning and AI applications. They are used in the computer industry to speed up the training of deep learning models and to enable faster inference. Tensor Cores are capable of performing matrix operations at a much faster rate than traditional CPUs, allowing for faster training and inference of deep learning models. This technology is used in a variety of applications, including image recognition, natural language processing, and autonomous driving. Tensor Cores are also used in the gaming industry to improve the performance of games and to enable more realistic graphics.

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