Jump to content

UHQBot

Forum Bot
  • Posts

    39,270
  • Joined

  • Last visited

  • Days Won

    25

Posts posted by UHQBot

  1. Leading users and industry-standard benchmarks agree: NVIDIA H100 Tensor Core GPUs deliver the best AI performance, especially on the large language models (LLMs) powering generative AI.

    H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That excellence is delivered both per-accelerator and at-scale in massive servers.

    For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and operated by CoreWeave, a cloud service provider specializing in GPU-accelerated workloads, the system completed the massive GPT-3-based training benchmark in less than eleven minutes.

    “Our customers are building state-of-the-art generative AI and LLMs at scale today, thanks to our thousands of H100 GPUs on fast, low-latency InfiniBand networks,” said Brian Venturo, co-founder and CTO of CoreWeave. “Our joint MLPerf submission with NVIDIA clearly demonstrates the great performance our customers enjoy.”

    Top Performance Available Today

    Inflection AI harnessed that performance to build the advanced LLM behind its first personal AI, Pi, which stands for personal intelligence. The company will act as an AI studio, creating personal AIs users can interact with in simple, natural ways.

    “Anyone can experience the power of a personal AI today based on our state-of-the-art large language model that was trained on CoreWeave’s powerful network of H100 GPUs,” said Mustafa Suleyman, CEO of Inflection AI.

    Co-founded in early 2022 by Mustafa and Karén Simonyan of DeepMind and Reid Hoffman, Inflection AI aims to work with CoreWeave to build one of the largest computing clusters in the world using NVIDIA GPUs.

    Tale of the Tape

    These user experiences reflect the performance demonstrated in the MLPerf benchmarks announced today.

    NVIDIA wins all eight tests in MLPerf Training v3.0

    H100 GPUs delivered the highest performance on every benchmark, including large language models, recommenders, computer vision, medical imaging and speech recognition. They were the only chips to run all eight tests, demonstrating the versatility of the NVIDIA AI platform.

    Excellence Running at Scale

    Training is typically a job run at scale by many GPUs working in tandem. On every MLPerf test, H100 GPUs set new at-scale performance records for AI training.

    Optimizations across the full technology stack enabled near linear performance scaling on the demanding LLM test as submissions scaled from hundreds to thousands of H100 GPUs.

    NVIDIA demonstrates efficiency at scale in MLPerf Training v3.0

    In addition, CoreWeave delivered from the cloud similar performance to what NVIDIA achieved from an AI supercomputer running in a local data center. That’s a testament to the low-latency networking of the NVIDIA Quantum-2 InfiniBand networking CoreWeave uses.

    In this round, MLPerf also updated its benchmark for recommendation systems.

    The new test uses a larger data set and a more modern AI model to better reflect the challenges cloud service providers face. NVIDIA was the only company to submit results on the enhanced benchmark.

    An Expanding NVIDIA AI Ecosystem

    Nearly a dozen companies submitted results on the NVIDIA platform in this round. Their work shows NVIDIA AI is backed by the industry’s broadest ecosystem in machine learning.

    Submissions came from major system makers that include ASUS, Dell Technologies, GIGABYTE, Lenovo, and QCT. More than 30 submissions ran on H100 GPUs.

    This level of participation lets users know they can get great performance with NVIDIA AI both in the cloud and in servers running in their own data centers.

    Performance Across All Workloads

    NVIDIA ecosystem partners participate in MLPerf because they know it’s a valuable tool for customers evaluating AI platforms and vendors.

    The benchmarks cover workloads users care about — computer vision, translation and reinforcement learning, in addition to generative AI and recommendation systems.

    Users can rely on MLPerf results to make informed buying decisions, because the tests are transparent and objective. The benchmarks enjoy backing from a broad group that includes Arm, Baidu, Facebook AI, Google, Harvard, Intel, Microsoft, Stanford and the University of Toronto.

    MLPerf results are available today on H100, L4 and NVIDIA Jetson platforms across AI training, inference and HPC benchmarks. We’ll be making submissions on NVIDIA Grace Hopper systems in future MLPerf rounds as well.

    The Importance of Energy Efficiency

    As AI’s performance requirements grow, it’s essential to expand the efficiency of how that performance is achieved. That’s what accelerated computing does.

    Data centers accelerated with NVIDIA GPUs use fewer server nodes, so they use less rack space and energy. In addition, accelerated networking boosts efficiency and performance, and ongoing software optimizations bring x-factor gains on the same hardware.

    Energy-efficient performance is good for the planet and business, too. Increased performance can speed time to market and let organizations build more advanced applications.

    Energy efficiency also reduces costs because data centers accelerated with NVIDIA GPUs use fewer server nodes. Indeed, NVIDIA powers 22 of the top 30 supercomputers on the latest Green500 list.

    Software Available to All

    NVIDIA AI Enterprise, the software layer of the NVIDIA AI platform, enables optimized performance on leading accelerated computing infrastructure. The software comes with the enterprise-grade support, security and reliability required to run AI in the corporate data center.

    All the software used for these tests is available from the MLPerf repository, so virtually anyone can get these world-class results.

    Optimizations are continuously folded into containers available on NGC, NVIDIA’s catalog for GPU-accelerated software.

    Read this technical blog for a deeper dive into the optimizations fueling NVIDIA’s MLPerf performance and efficiency.

    View the full article

  2. Editor’s note: This post is a part of our Meet the Omnivore series, which features individual creators and developers who accelerate 3D workflows and create virtual worlds using NVIDIA Omniverse, a development platform built on Universal Scene Description, aka OpenUSD.

    As augmented reality (AR) becomes more prominent and accessible across the globe, Kiryl Sidarchuk is helping to erase the border between the real and virtual worlds.

    kiryl-sidarchuk-headshot-150x150.jpgKiryl Sidarchuk

    Co-founder and CEO of AR-Generation, which is a member of the NVIDIA Inception program for cutting-edge startups, Sidarchuk with his company developed MagiScan, an AI-based 3D scanner app.

    It lets users capture any object with their smartphone camera and quickly creates a high-quality, detailed 3D model of it for use in any AR or metaverse application.

    AR-Generation now offers an extension that enables direct export of 3D models from MagiScan to NVIDIA Omniverse, a development platform for connecting and building 3D tools and metaverse applications.

    It’s made possible with speed and ease by Universal Scene Description, aka OpenUSD, an extensible framework that serves as a common language between digital content-creation tools.

    “Augmented reality will become an integral part of everyday life,” said Sidarchuk, who’s based in Nicosia, Cyprus. “We customized our app to allow export of 3D models based on real-world objects directly to Omniverse, enabling users to showcase the models in AR and integrate them into any metaverse or game.”

    Omniverse extensions are core building blocks that let anyone create and extend functions of Omniverse apps using the popular Python or C++ programming languages.

    It was simple and convenient for AR-Generation to build the extension, Sidarchuk said, thanks to easily accessible documentation, as well as technical guidance from NVIDIA teams, free AWS credits and networking opportunities with other AI-driven companies — all benefits of being a part of NVIDIA Inception.

    Capture, Click and Create 3D Models From Real-World Objects 

    Sidarchuk estimates that MagiScan can create 3D models from objects 10x faster and at up to 100x less cost than it would take a designer to do so manually.

    This frees creators up to focus on fine-tuning their work and makes AR more accessible to all through a simple app.

    AR-Generation chose to build an extension for Omniverse because the platform “provides a convenient environment that integrates all the tools for working with 3D and generative AI,” said Sidarchuk. “Plus, we can collaborate and exchange ideas with colleagues in real time.”

    magiscan-to-omniverse.pngExport 3D models from MagiScan to Omniverse with OpenUSD.

    Sidarchuk’s favorite feature of Omniverse is its OpenUSD compatibility, which enables seamless interchange of 3D data between creative applications. “OpenUSD is the format of the future,” he said.

    Based on this framework, the MagiScan extension for Omniverse enables fast, affordable creation of high-quality 3D models for any object. MagiScan is available for download on iOS and Android devices.

    “It can help everyone from individuals to large corporations save time and money in digitalization,” said Sidarchuk, who claims his first word as a toddler was “money.”

    The business-oriented developer started his first company at age 16. It was a one-man endeavor, buying fresh fruits and vegetables from a small village and selling them in Minsk, the capital of Belarus. “That’s how I earned enough to buy my first car,” he mused.

    More than a dozen years later, when he’s not working to “enhance human capabilities through augmented-reality technologies,” he said, Sidarchuk now spends his free time with his five-year-old daughter, Aurora.

    Watch Sidarchuk discuss 3D modeling, AI and AR on a replay of his Omniverse livestream on demand, and learn more about the MagiScan extension for Omniverse.

    Join In on the Creation

    Anyone can build their own Omniverse extension or Connector to enhance their 3D workflows and tools. Creators and developers across the world can download NVIDIA Omniverse for free, and enterprise teams can use the platform for their 3D projects.

    Check out artwork from other “Omnivores” and submit projects in the gallery. Connect your workflows to Omniverse with software from Adobe, Autodesk, Epic Games, Maxon, Reallusion and more.

    Get started with NVIDIA Omniverse by downloading the standard license free, or learn how Omniverse Enterprise can connect your team. Developers can get started with Omniverse resources and learn about OpenUSD. Explore the growing ecosystem of 3D tools connected to Omniverse.

    Stay up to date on the platform by subscribing to the newsletter, and follow NVIDIA Omniverse on Instagram, Medium and Twitter. For more, join the Omniverse community and check out the Omniverse forums, Discord server, Twitch and YouTube channels. 

    View the full article

  3. While generative AI is a relatively new household term, drug discovery company Insilico Medicine has been using it for years to develop new therapies for debilitating diseases.

    The company’s early bet on deep learning is bearing fruit — a drug candidate discovered using its AI platform is now entering Phase 2 clinical trials to treat idiopathic pulmonary fibrosis, a relatively rare respiratory disease that causes progressive decline in lung function.

    Insilico used generative AI for each step of the preclinical drug discovery process: to identify a molecule that a drug compound could target, generate novel drug candidates, gauge how well these candidates would bind with the target, and even predict the outcome of clinical trials.

    Doing this using traditional methods would have cost more than $400 million and taken up to six years. But with generative AI, Insilico accomplished them for one-tenth of the cost and one-third of the time — reaching the first phase of clinical trials just two and a half years after beginning the project.

    “This first drug candidate that’s going to Phase 2 is a true highlight of our end-to-end approach to bridge biology and chemistry with deep learning,” said Alex Zhavoronkov, CEO of Insilico Medicine. “This is a significant milestone not only for us, but for everyone in the field of AI-accelerated drug discovery.”

    Insilico is a premier member of NVIDIA Inception, a free program that provides cutting-edge startups with technical training, go-to-market support and AI platform guidance. The company uses NVIDIA Tensor Core GPUs in its generative AI drug design engine, Chemistry42, to generate novel molecular structures — and was one of the first adopters of an early precursor to NVIDIA DGX systems in 2015.

    AI Enables End-to-End Preclinical Drug Discovery

    Insilico’s Pharma.AI platform includes multiple AI models trained on millions of data samples for a range of tasks. One AI tool, PandaOmics, rapidly identifies and prioritizes targets that play a significant role in a disease’s effectiveness — like the infamous spike protein on the virus that causes COVID-19.

    The Chemistry42 engine can design within days new potential drug compounds that target the protein identified using PandaOmics. The generative chemistry tool uses deep learning to come up with drug-like molecular structures from scratch.

    “Typically, AI companies in drug discovery focus either on biology or on chemistry,” said Petrina Kamya, head of AI platforms at Insilico. “From the start, Insilico has been applying the same deep learning approach to both fields, using AI both to discover drug targets and generate chemical structures of small molecules.”

    Over the years, the Insilico team has adopted different kinds of deep neural networks for drug discovery, including generative adversarial networks and transformer models. They’re now using NVIDIA BioNeMo to accelerate the early drug discovery process with generative AI.

    Finding the Needle in the AI Stack

    To develop its pulmonary fibrosis drug candidate, Insilico used Pharma.AI to design and synthesize about 80 molecules, achieving unprecedented success rates for preclinical drug candidates. The process — from identifying the target to nominating a promising drug candidate for trials — took under 18 months.

    During Phase 2 clinical trials, Insilico’s pulmonary fibrosis drug will be tested in several hundred people with the condition in the U.S. and China. The process will take several months — but in parallel, the company has more than 30 programs in the pipeline to target other diseases, including a number of cancer drugs.

    “When we first presented our results, people just did not believe that generative AI systems could achieve this level of diversity, novelty and accuracy,” said Zhavoronkov. “Now that we have an entire pipeline of promising drug candidates, people are realizing that this actually works.”

    Learn more about Insilico Medicine’s Chemistry42 platform for AI-accelerated drug candidate screening in this talk from NVIDIA GTC.

    Subscribe to NVIDIA healthcare news and generative AI news.

    View the full article

×
×
  • Create New...

Important Information

By using this site, you agree to our Guidelines Privacy Policy.