NVIDIA Makes Grace Blackwell Accessible to AI Developers Everywhere

NVIDIA Launches Project DIGITS: Your Personal AI Supercomputer

nVidia has just pulled back the curtain on something pretty exciting:

Project DIGITS

, a personal AI supercomputer designed to make technology accessible to AI researchers, data scientists, and students around the globe. Imagine having the power of the NVIDIA Grace Blackwell platform right at your fingertips! With the introduction of the GB10 Grace Blackwell Superchip, this project promises to deliver a staggering petaflop of AI computing performance, perfect for prototyping, fine-tuning, and running those massive AI models we often hear about.

But what does this really mean for you? Picture this: you’re working on your desktop system, developing and running inference on your models, and when you’re ready, you can seamlessly deploy them on a cloud or data center infrastructure. It’s like having your own AI lab without the hefty price tag of traditional supercomputers. As Jensen Huang, NVIDIA’s founder and CEO, put it, “AI will be mainstream in every application for every industry.” With Project DIGITS, that future is not just a dream—it’s becoming a reality for millions of developers.

The Power of the GB10 Superchip

Let’s dive into the heart of Project DIGITS: the GB10 Superchip. This isn’t just any chip; it’s a system-on-a-chip (SoC) that leverages the Grace Blackwell architecture to deliver up to 1 petaflop of AI performance at FP4 precision. What does that mean for you? Well, it means you can run incredibly complex AI tasks from the comfort of your desk, all powered by a chip that’s designed for maximum efficiency.

The GB10 features an NVIDIA Blackwell GPU, complete with the latest-generation CUDA cores and fifth-generation Tensor Cores. These are connected via NVLink, a chip-to-chip interconnect, to a high-performance NVIDIA Grace CPU that boasts 20 power-efficient cores built on the ARM architecture. With collaboration from MediaTek, known for its expertise in Arm-based SoC designs, the GB10 is all about delivering top-notch power efficiency and performance. And the best part? It can run off a standard electrical outlet—no need for a massive power setup.

Each Project DIGITS unit comes packed with 128 GB of unified memory and up to 4 TB of NVMe storage. This means you can run up to 200-billion-parameter large language models, pushing the boundaries of AI innovation. Plus, if you want to take it a step further, you can link two Project DIGITS supercomputers using NVIDIA ConnectX networking to tackle models with up to 405 billion parameters. Talk about scaling up!

AI Supercomputing Made Easy

With the Grace Blackwell architecture, Project DIGITS makes it easier than ever for enterprises and researchers to prototype, fine-tune, and test their models. You can run everything on a local Project DIGITS system using the Linux-based NVIDIA DGX OS and then effortlessly deploy your creations onto NVIDIA DGX Cloud or any accelerated cloud instance. It’s all about keeping that workflow smooth and efficient.

What’s even better? You’ll have access to a treasure trove of NVIDIA AI software for all your experimentation and prototyping needs. From software development kits to orchestration tools and frameworks, the NVIDIA NGC catalog and the NVIDIA Developer portal have got you covered. Want to fine-tune your models? The NVIDIA NeMo framework has your back. Need to speed up your data science projects? Check out the NVIDIA RAPIDS libraries. And yes, you can run popular frameworks like PyTorch, Python, and Jupyter notebooks.

For those looking to build advanced AI applications, NVIDIA Blueprints and NVIDIA NIM microservices are available for research, development, and testing through the NVIDIA Developer Program. And when it’s time to move your AI applications from experimentation to production, the NVIDIA AI Enterprise license ensures you have enterprise-grade security, support, and the latest software updates.

Get Ready for Project DIGITS

So, when can you get your hands on this game-changing technology? Project DIGITS is set to launch in May, with a starting price of $3,000. If you’re as excited as we are, make sure to sign up for notifications to stay in the loop. This is your chance to bring the power of AI supercomputing right to your desk, unlocking endless possibilities for innovation and creativity. Are you ready to shape the future of AI?

Background Information

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About ARM:

ARM, originally known as Acorn RISC Machine, is a British semiconductor and software design company that specializes in creating energy-efficient microprocessors, system-on-chip (SoC) designs, and related technologies. Founded in 1990, ARM has become a important player in the global semiconductor industry and is widely recognized for its contributions to mobile computing, embedded systems, and Internet of Things (IoT) devices. ARM's microprocessor designs are based on the Reduced Instruction Set Computing (RISC) architecture, which prioritizes simplicity and efficiency in instruction execution. This approach has enabled ARM to produce highly efficient and power-saving processors that are used in a vast array of devices, ranging from smartphones and tablets to IoT devices, smart TVs, and more. The company does not manufacture its own chips but licenses its processor designs and intellectual property to a wide range of manufacturers, including Qualcomm, Apple, Samsung, and NVIDIA, who then integrate ARM's technology into their own SoCs. This licensing model has contributed to ARM's widespread adoption and influence across various industries.

  

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About MediaTek:

MediaTek Inc. is a Taiwanese semiconductor company that designs and manufactures a wide range of system-on-chip (SoC) solutions for various consumer electronics, including smartphones, tablets, smart TVs, wearables, and IoT (Internet of Things) devices. Founded in 1997, MediaTek has become a important player in the semiconductor industry, known for its innovative chipsets that offer a balance of performance, power efficiency, and affordability.

  

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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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Blackwell: Blackwell is an AI computing architecture designed to supercharge tasks like training large language models. These powerful GPUs boast features like a next-gen Transformer Engine and support for lower-precision calculations, enabling them to handle complex AI workloads significantly faster and more efficiently than before. While aimed at data centers, the innovations within Blackwell are expected to influence consumer graphics cards as well

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CPU: The Central Processing Unit (CPU) is the brain of a computer, responsible for executing instructions and performing calculations. It is the most important component of a computer system, as it is responsible for controlling all other components. CPUs are used in a wide range of applications, from desktop computers to mobile devices, gaming consoles, and even supercomputers. CPUs are used to process data, execute instructions, and control the flow of information within a computer system. They are also used to control the input and output of data, as well as to store and retrieve data from memory. CPUs are essential for the functioning of any computer system, and their applications in the computer industry are vast.

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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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Grace Blackwell: Grace Blackwell is a cutting-edge technology that has revolutionized the computer industry. It is a type of artificial intelligence that is designed to mimic human cognitive abilities, such as learning, problem-solving, and decision-making. This technology has been applied in various areas of the computer industry, including data analysis, natural language processing, and machine learning. For example, Grace Blackwell can analyze large amounts of data and identify patterns and trends, making it a valuable tool for businesses to make informed decisions. It can also understand and respond to human language, making it useful for virtual assistants and chatbots. Additionally, Grace Blackwell can continuously learn and improve its performance, making it an invaluable asset in the development of new technologies. Overall, Grace Blackwell has greatly enhanced the capabilities of computers and has opened up new possibilities for the future of technology.

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NVMe: Non-Volatile Memory Express (NVMe) is a newly developed technology that has been gaining traction in the computer industry. This technology is a standard interface which allows for high-speed storage and retrieval of data from solid state drives (SSDs). NVMe is designed to increase the speed of data transfers in storage systems by enabling a direct connection to PCI Express (PCIe) bus, resulting in significantly faster access times compared to traditional interface protocols such SSDs. NVMe is particularly useful for applications that require lightning-fast access to large amounts of high-value data. NVMe-based SSDs are being widely adopted in the computer industry and are being employed to power data centers, high-end workstations, and gaming machines to support lightning-fast data processing and retrieval, which unlocks possibilities for machine learning, real-time analytics, edge computing, and other cutting-edge applications. NVMe is proving to be an invaluable tool in the field of computing, offering immense

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SoC: A System-on-Chip (SoC) is a highly integrated semiconductor device that encapsulates various electronic components, including processors, memory, input/output interfaces, and often specialized hardware components, all on a single chip. SoCs are designed to provide a complete computing system or subsystem within a single chip package, offering enhanced performance, power efficiency, and compactness. They are commonly used in a wide range of devices, from smartphones and tablets to embedded systems and IoT devices, streamlining hardware complexity and facilitating efficient integration of multiple functions onto a single chip.

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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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