NVIDIA AI Workbench Speeds Adoption of Custom Generative AI for Worlds Enterprises

TensorRT-LLM is built on the FasterTransformer project, with improved flexibility and closer pairing with NVIDIA Triton Inference Server for greater end-to-end performance on state-of-the-art LLMs. Create enterprise-grade models that protect privacy, data security, and intellectual property. If you want to benefit from the AI, you can check our data-driven lists for AI platforms, consultants and companies.

Generative AI could be a pivotal tool to help government bodies work within budget constraints, deliver government services more quickly and achieve positive public sentiment. The analytical capabilities of AI can also help process documents to speed the delivery of vital services provided by organizations like Medicare, Medicaid, Veterans Affairs, USPS and the State Department. From intelligent tutoring systems to automated essay grading, AI has been employed in education for decades. As universities use AI to improve teacher and student experiences, they’re increasingly dedicating resources to build AI-focused research initiatives.

This type of trainable foundation model enables scientists to create variants for research into specific diseases, allowing them to develop target treatments for rare conditions. New NVIDIA AI Enterprise 4.0 Software Advances AI Deployment To further accelerate the adoption of generative AI, NVIDIA announced the latest version of its enterprise software platform, NVIDIA AI Enterprise 4.0. Developers with a Windows or Linux-based NVIDIA RTX™ PC or workstation will also be able to initiate, test and fine-tune enterprise-grade generative AI projects on their local RTX systems, and easily access data center and cloud computing resources to scale as needed. The Search-based Interest Model (SIM) is a system that predicts user behavior based on sequences of previous interactions. The original model has a cascaded two-stage search mechanism that enhances SIM’s ability to model lifelong sequential behavior data in both scalability and accuracy.

Immunai CTO and Co-Founder Luis Voloch on Using Deep Learning to Develop New Drugs Luis Voloch talks about tackling the challenges of the immune system with a machine learning and data science mindset. It can, for example, be fed DNA, RNA, viral and bacterial data to craft a model that understands the language of genomes. That model can help predict dangerous coronavirus variants to accelerate drug and vaccine research. These linear and time-consuming design concept processes are utilized for exterior parts like grilles, hoods and wheels, as well as interior aspects such as dashboards, seats, ergonomics and user interfaces.

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The app also allows users to share their generated art with members of the WOMBO community. Their cost-effective method enables filmmakers, production studios, and artists to partner with CGI specialists much earlier in the post-production process. Bring your own foundation model for visual design, ‌optimized by NVIDIA AI experts to run at fast inference speeds on DGX Cloud.

Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists. Jio has broad expertise, infrastructure and engineering skill to roll out and manage the new AI computing infrastructure. The collaboration with NVIDIA also aligns with Yakov Livshits its strategy of serving as a large, comprehensive digital, cloud and networking platform for both consumers and business customers. Learn more about how to use NVIDIA Morpheus to detect spear phishing e-mails faster and with greater accuracy using the NVIDIA AI workflow example. You can also apply to try NVIDIA Morpheus in LaunchPad and request a 90-day free trial to test drive NVIDIA Morpheus, part of the NVIDIA AI Enterprise software family.

NVIDIA Morpheus Helps Defend Against Spear Phishing with Generative AI

Developers with an NVIDIA RTX PC or workstation can also launch, test, and fine-tune enterprise-grade generative AI projects on their local systems, and access data center and cloud computing resources when scaling up. New NVIDIA AI Enterprise 4.0 Software Advances AI Deployment To further accelerate the adoption of generative AI, NVIDIA announced the latest version of its enterprise software platform, NVIDIA AI Enterprise 4.0. It gives businesses the tools needed to adopt generative AI, while also offering the security and API stability required for reliable production deployments. Using NVIDIA BioNeMo models, Amgen, a global leader in biotechnology, has slashed the time it takes to customize models for molecule screening and optimization from three months to just a few weeks.

It uses pretrained LLMs, NeMo, NVIDIA Triton Inference Server, along with third-party tools including Langchain and vector database, for training and deploying the knowledge base question-answering system. BERT-based models are pretrained on massive amounts of text data using the bidirectional transformer architecture, Yakov Livshits which allows them to capture context from both left and right directions. This pretraining enables these models to perform well on various natural language processing tasks such as sentiment analysis and sentence prediction—without requiring task-specific architecture modifications or extensive fine-tuning.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Creators of all levels can tap into these resources to produce high-quality outputs that meet the growing demands for content and virtual worlds in the metaverse. Based on the Universal Scene Description (USD) framework, the NVIDIA Omniverse platform — which enables the development of metaverse applications — is expanding with Blender Yakov Livshits enhancements and a new suite of experimental generative AI tools for 3D artists. NVIDIA, Deutsche Bank, Bloomberg and others are creating LLMs trained on domain-specific and proprietary data to power finance applications. Businesses that previously dabbled in AI are now rushing to adopt and deploy the latest applications.

London advertising giant WPP and NVIDIA are working on a groundbreaking generative AI-enabled content engine to assist the $700 billion digital advertising industry. Other automotive industry players are also looking to generative AI to help accelerate design iterations and provide better results. Spear phishing e-mails are indistinguishable from benign e-mails, with the only difference between the scam and legitimate e-mail being the intent of the sender.

Generative AI is impacting every industry today—from renewable energy forecasting and drug discovery to fraud prevention and wildfire detection. Putting generative AI into practice will help increase productivity, automate tasks, and unlock new opportunities. Leverage the world’s most powerful accelerators for generative AI, optimized for training and deploying LLMs. Confidently deploy accelerated infrastructure that securely and optimally runs generative AI workloads.

Users must get the local environment set up with the appropriate NVIDIA software, such as NVIDIA TensorRT and NVIDIA Triton. Then, they need models from Hugging Face, code from GitHub, and containers from NVIDIA NGC. Finally, they must configure the container, handle apps like JupyterLab, and make sure their GPUs support the model size. While Gradio apps on services like Hugging Face Spaces provide one-click interaction with models like StableDiffusion XL, getting those models and apps to run locally can be tough.

These models use innovative architectures and frameworks to achieve high accuracy across a wide range of complexities in each task type. NVIDIA is at the forefront of generative AI research, launching groundbreaking models like StyleGAN, GauGAN, eDiff-I, and many more. These generative models are pretrained for efficient enterprise application development. Get started today with highly accurate models that span diverse use cases and domains, including computer vision, speech, language understanding, molecule generation, and more, and that can be customized for specific tasks. Developers can build generative AI tools for 3D worlds with Omniverse’s modular development framework and enterprises can leverage the latest generative AI technologies to scale digital twin simulations with NVIDIA Omniverse Enterprise.

Users can reimagine digital worlds and animate lifelike characters with new depths of creativity through the bridging of audio-to-animation tools, generative AI and the metaverse. Omniverse Launcher, the portal to download Omniverse content and reference applications, has also been made available to system builders so they can preinstall it, enabling optimized, out-of-the-box experiences for 3D creators on NVIDIA Studio-validated laptops. GIGABYTE and AORUS will be the first laptops launching in 2023 with Omniverse Launcher preinstalled, expanding platform access to a growing number of 3D content creators. Learn more about powerful generative AI tools to help your business increase productivity, automate tasks, and unlock new opportunities for employees and customers. With large edge infrastructure and access to vast datasets, telcos around the world are now offering generative AI as a service to enterprise and government customers.

• Developers can use the models offered on each service through simple application programming interfaces (APIs).
• Configured for a sporty, rear-wheel drive, its modular design will also be scalable for other vehicle segments.
• “We’re at an inflection point where accelerated computing and generative AI have come together to speed innovation at an unprecedented pace,” Huang said.

For example, Stable Diffusion performance is improved by 2x compared to the previous interference times for developers taking advantage of DirectML optimized paths. When optimized for GeForce RTX and NVIDIA RTX GPUs, which offer up to 1,400 Tensor TFLOPS for AI inferencing, generative AI models can run up to 5x faster than on competing devices. This is thanks to Tensor Cores — dedicated hardware in RTX GPUs built to accelerate AI calculations — and regular software improvements. Enhancements introduced last week at the Microsoft Build conference doubled performance for generative AI models, such as Stable Diffusion, that take advantage of new DirectML optimizations. At CES, NVIDIA unveiled new generative AI technologies coming to Omniverse to help create virtual worlds faster and easier than ever.