#dgx cloud

[empresa-journal]

NVIDIA (NVDA) joins the Trillion Dollar Club

NVIDIA has joined Alphabet (GOOG), Apple (AAPL), and Microsoft (MSFT) in the trillion dollar club. NVIDIA (NVDA) achieved a $1 trillion market capitalization on 30 May 2023. NVIDIA’s stock price will have to stay over $404.86 to keep the trillion-dollar value, CNBC reports. Thus, NVIDIA is close to regaining $1 trillion status because Mr. Market was paying $391.71 for its shares on 5 June…

[jcmarchi]

Generative AI Playgrounds: Pioneering the Next Generation of Intelligent Solution

New Post has been published on https://thedigitalinsider.com/generative-ai-playgrounds-pioneering-the-next-generation-of-intelligent-solution/

Generative AI Playgrounds: Pioneering the Next Generation of Intelligent Solution

Generative AI has gained significant traction due to its ability to create content that mimics human creativity. Despite its vast potential, with applications ranging from generating text and images to composing music and writing code, interacting with these rapidly evolving technologies remains daunting. The complexity of generative AI models and the technical expertise required often create barriers for individuals and small businesses who could benefit from it. To address this challenge, generative AI playgrounds are emerging as essential tools for democratizing access to these technologies.

What is Generative AI Playground

Generative AI playgrounds are intuitive platforms that facilitate interaction with generative models. They enable users to experiment and refine their ideas without requiring extensive technical knowledge. These environments provide developers, researchers, and creatives with an accessible space to explore AI capabilities, supporting activities such as rapid prototyping, experimentation and customization. The main goal of these playgrounds is to democratize access to advanced AI technologies, making it easier for users to innovate and experiment. Some of the leading generative AI playgrounds are:

Hugging Face: Hugging Face is a leading generative AI playground, especially renowned for its natural language processing (NLP) capabilities. It offers a comprehensive library of pre-trained AI models, datasets, and tools, making it easier to create and deploy AI applications. A key feature of Hugging Face is its transformers library, which includes a broad range of pre-trained models for tasks such as text classification, translation, summarization, and question-answering. Additionally, it provides a dataset library for training and evaluation, a model hub for discovering and sharing models, and an inference API for integrating models into real-time applications.

OpenAI’s Playground: The OpenAI Playground is a web-based tool that provides a user-friendly interface for experimenting with various OpenAI models, including GPT-4 and GPT-3.5 Turbo. It features three distinct modes to serve different needs: Chat Mode, which is ideal for building chatbot applications and includes fine-tuning controls; Assistant Mode, which equips developers with advanced development tools such as functions, a code interpreter, retrieval, and file handling for development tasks; and Completion Mode, which supports legacy models by allowing users to input text and view how the model completes it, with features like “Show probabilities” to visualize response likelihoods.

NVIDIA AI Playground: The NVIDIA AI Playground allows researchers and developers to interact with NVIDIA’s generative AI models directly from their browsers. Utilizing NVIDIA DGX Cloud, TensorRT, and Triton inference server, the platform offers optimized models that enhance throughput, reduce latency, and improve compute efficiency. Users can access inference APIs for their applications and research and run these models on local workstations with RTX GPUs. This setup enables high-performance experimentation and practical implementation of AI models in a streamlined fashion.

GitHub’s Models: GitHub has recently introduced GitHub Models, a playground aimed at increasing accessibility to generative AI models. With GitHub Models, users can explore, test, and compare models such as Meta’s Llama 3.1, OpenAI’s GPT-4o, Cohere’s Command, and Mistral AI’s Mistral Large 2 directly within the GitHub web interface. Integrated into GitHub Codespaces and Visual Studio Code, this tool streamlines the transition from AI application development to production. Unlike Microsoft Azure, which necessitates a predefined workflow and is available only to subscribers, GitHub Models offers immediate access, eliminating these barriers and providing a more seamless experience.

Amazon’s Party Rock: This generative AI playground, developed for Amazon’s Bedrock services, provides access to Amazon’s foundation AI models for building AI-driven applications. It offers a hands-on, user-friendly experience for exploring and learning about generative AI. With Amazon Bedrock, users can create a PartyRock app in three ways: start with a prompt by describing your desired app, which PartyRock will assemble for you; remix an existing app by modifying samples or apps from other users through the “Remix” option; or build from scratch with an empty app, allowing for complete customization of the layout and widgets.

The Potential of Generative AI Playgrounds

Generative AI playgrounds offer several key potentials that make them valuable tools for a wide range of users:

Accessibility: They lower the barrier to entry for working with complex generative AI models. This makes generative AI accessible to non-experts, small businesses, and individuals who might otherwise find it difficult to engage with these technologies.

Innovation: By providing user-friendly interfaces and pre-built models, these playgrounds encourage creativity and innovation, allowing users to quickly prototype and test new ideas.

Customization: Users can readily adopt generative AI models to their specific needs, experimenting with fine-tuning and modifications to create customized solutions that serve their unique requirements.

Integration: Many platforms facilitate integration with other tools and systems, making it easier to incorporate AI capabilities into existing workflows and applications.

Educational Value: These platforms serve as educational tools, helping users learn about AI technologies and how they work through hands-on experience and experimentation.

The Challenges of Generative AI Playgrounds

Despite the potential, generative AI platforms face several challenges:

The primary challenge is the technical complexity of generative AI models. While they aim to simplify interaction, advanced generative AI models require substantial computational resources and a deep understanding of their workings, especially for building custom applications. High-performance computing resources and optimized algorithms are essential to improve response and usability of these platforms.

Handling private data on these platforms also poses a challenge. Robust encryption, anonymization, and strict data governance are necessary to ensure privacy and security on these playgrounds, making them trustworthy.

For generative AI playgrounds to be truly useful, they must seamlessly integrate with existing workflows and tools. Ensuring compatibility with various software, APIs, and hardware can be complex, requiring ongoing collaboration with technology providers and adherence to new AI standards.

The rapid pace of AI advancements means these playgrounds must continuously evolve. They need to incorporate the latest models and features, anticipate future trends, and adapt quickly. Staying current and agile is crucial in this fast-moving field.

The Bottom Line

Generative AI playgrounds are paving the way for broader access to advanced AI technologies. By offering intuitive platforms like Hugging Face, OpenAI’s Playground, NVIDIA AI Playground, GitHub Models, and Amazon’s Party Rock, these tools enable users to explore and experiment with AI models without needing deep technical expertise. However, the road ahead is not without hurdles. Ensuring these platforms handle complex models efficiently, protect user data, integrate well with existing tools, and keep up with rapid technological changes will be crucial. As these playgrounds continue to develop, their ability to balance user-friendliness with technical depth will determine their impact on innovation and accessibility.

[govindhtech]

NVIDIA AI Foundry Custom Models NeMo Retriever microservice

How Businesses Can Create Personalized Generative AI Models with NVIDIA AI Foundry.

NVIDIA AI Foundry

Companies looking to use  AI need specialized Custom models made to fit their particular sector requirements.

With the use of software tools, accelerated computation, and data, businesses can build and implement unique models with NVIDIA  AI Foundry, a service that may significantly boost their generative AI projects.

Similar to how TSMC produces chips made by other firms, NVIDIA AI Foundry offers the infrastructure and resources needed by other businesses to create and modify AI models. These resources include DGX Cloud, foundation models, NVIDIA NeMo software, NVIDIA knowledge, ecosystem tools, and support.

The product is the primary distinction: NVIDIA AI Foundry assists in the creation of Custom models, whereas TSMC manufactures actual semiconductor chips. Both foster creativity and provide access to a huge network of resources and collaborators.

Businesses can use AI Foundry to personalise NVIDIA and open Custom models models, such as NVIDIA Nemotron, CodeGemma by Google DeepMind, CodeLlama, Gemma by Google DeepMind, Mistral, Mixtral, Phi-3, StarCoder2, and others. This includes the recently released Llama 3.1 collection.

AI Innovation is Driven by Industry Pioneers

Among the first companies to use NVIDIA AI Foundry are industry leaders Amdocs, Capital One, Getty Images, KT, Hyundai Motor Company, SAP, ServiceNow, and Snowflake. A new era of AI-driven innovation in corporate software, technology, communications, and media is being ushered in by these trailblazers.

According to Jeremy Barnes, vice president of AI Product at ServiceNow, “organizations deploying AI can gain a competitive edge with Custom models that incorporate industry and business knowledge.” “ServiceNow is refining and deploying models that can easily integrate within customers’ existing workflows by utilising NVIDIA AI Foundry.”

The NVIDIA AI Foundry’s Foundation

The foundation models, corporate software, rapid computing, expert support, and extensive partner ecosystem are the main pillars that underpin NVIDIA AI Foundry.

Its software comprises the whole software platform for expediting model building, as well as AI foundation models from NVIDIA and the  AI community.

NVIDIA DGX Cloud, a network of accelerated compute resources co-engineered with the top public clouds in the world Amazon Web Services, Google  Cloud, and Oracle  Cloud Infrastructure is the computational powerhouse of NVIDIA  AI Foundry. Customers of AI Foundry may use DGX Cloud to grow their AI projects as needed without having to make large upfront hardware investments.

They can also create and optimize unique generative AI applications with previously unheard-of ease and efficiency. This adaptability is essential for companies trying to remain nimble in a market that is changing quickly.

NVIDIA AI Enterprise specialists are available to support customers of NVIDIA AI Foundry if they require assistance. In order to ensure that the models closely match their business requirements, NVIDIA experts may guide customers through every stage of the process of developing, optimizing, and deploying their models using private data.

Customers of NVIDIA AI Foundry have access to a worldwide network of partners who can offer a comprehensive range of support. Among the NVIDIA partners offering AI Foundry consulting services are Accenture, Deloitte, Infosys, and Wipro. These services cover the design, implementation, and management of AI-driven digital transformation initiatives. Accenture is the first to provide the Accenture AI Refinery framework, an AI Foundry-based solution for creating Custom models.

Furthermore, companies can get assistance from service delivery partners like Data Monsters, Quantiphi, Slalom, and SoftServe in navigating the challenges of incorporating AI into their current IT environments and making sure that these applications are secure, scalable, and in line with business goals.

Using AIOps and MLOps platforms from NVIDIA partners, such as Cleanlab, DataDog, Dataiku, Dataloop, DataRobot, Domino Data Lab, Fiddler AI, New Relic, Scale, and Weights & Biases, customers may create production-ready NVIDIA AI Foundry models.

Nemo retriever microservice

Clients can export their AI Foundry models as NVIDIA NIM inference microservices, which can be used on their choice accelerated infrastructure. These microservices comprise the Custom models, optimized engines, and a standard API.

NVIDIA TensorRT-LLM and other inferencing methods increase Llama 3.1 model efficiency by reducing latency and maximizing throughput. This lowers the overall cost of operating the models in production and allows businesses to create tokens more quickly. The NVIDIA  AI Enterprise software bundle offers security and support that is suitable for an enterprise.

Along with cloud instances from Amazon Web Services, Google Cloud, and Oracle  Cloud Infrastructure, the extensive array of deployment options includes NVIDIA-Certified Systems from worldwide server manufacturing partners like Cisco, Dell, HPE, Lenovo, and Supermicro.

Furthermore, Together  AI, a leading cloud provider for AI acceleration, announced today that it will make Llama 3.1 endpoints and other open models available on DGX  Cloud through the usage of its NVIDIA GPU-accelerated inference stack, which is accessible to its ecosystem of over 100,000 developers and businesses.

According to Together AI’s founder and CEO, Vipul Ved Prakash, “every enterprise running generative AI applications wants a faster user experience, with greater efficiency and lower cost.” “With NVIDIA DGX Cloud, developers and businesses can now optimize performance, scalability, and security by utilising the Together Inference Engine.”

NVIDIA NeMo

NVIDIA NeMo Accelerates and Simplifies the Creation of Custom Models

Developers can now easily curate data, modify foundation models, and assess performance using the capabilities provided by NVIDIA NeMo integrated into AI Foundry. NeMo technologies consist of:

A GPU-accelerated data-curation package called NeMo Curator enhances the performance of generative AI models by preparing large-scale, high-quality datasets for pretraining and fine-tuning.

NeMo Customizer is a scalable, high-performance microservice that makes it easier to align and fine-tune LLMs for use cases specific to a given domain.

On any accelerated cloud or data centre, NeMo Evaluator offers autonomous evaluation of generative AI models across bespoke and academic standards.

NeMo Guardrails is a dialogue management orchestrator that supports security, appropriateness, and correctness in large-scale language model smart applications, hence offering protection for generative AI applications.

Businesses can construct unique AI models that are perfectly matched to their needs by utilising the NeMo platform in NVIDIA AI Foundry.

Better alignment with strategic objectives, increased decision-making accuracy, and increased operational efficiency are all made possible by this customization.

For example, businesses can create models that comprehend industry-specific vernacular, adhere to legal specifications, and perform in unison with current processes.

According to Philipp Herzig, chief  AI officer at SAP, “as a next step of their partnership, SAP plans to use NVIDIA’s NeMo platform to help businesses to accelerate AI-driven productivity powered by SAP Business  AI.”

NeMo Retriever

NeMo Retriever microservice

Businesses can utilize NVIDIA NeMo Retriever NIM inference microservices to implement their own AI models in a live environment. With retrieval-augmented generation (RAG), these assist developers in retrieving private data to provide intelligent solutions for their AI applications.

According to Baris Gultekin, Head of AI at Snowflake, “safe, trustworthy AI is a non-negotiable for enterprises harnessing generative AI, with retrieval accuracy directly impacting the relevance and quality of generated responses in RAG systems.” “NeMo Retriever, a part of NVIDIA AI Foundry, is leveraged by Snowflake Cortex AI to further provide enterprises with simple, reliable answers using their custom data.”

Custom Models

Custom Models Provide a Competitive Edge

The capacity of NVIDIA AI Foundry to handle the particular difficulties that businesses encounter while implementing AI is one of its main benefits. Specific business demands and data security requirements may not be fully satisfied by generic AI models. On the other hand, Custom models are more flexible, adaptable, and perform better, which makes them perfect for businesses looking to get a competitive edge.

Read more on govindhtech.com

[tumnikkeimatome]

NVIDIAと共同開発された12Bパラメータモデル『Mistral NeMo』専用のTekkenトークナイザーで100言語以上で優れた圧縮効率を実現、量子化でも性能低下を抑制

Mistral NeMoの概要 Mistral AIとNVIDIAが共同開発した大規模言語モデル「Mistral NeMo」が2024年7月18日に発表されました。 120億のパラメータを持つこのモデルは、128,000トークンという長いコンテキストウィンドウを特徴としています。 同サイズカテゴリーにおいて、推論能力、世界知識、コーディング精度で最高水準の性能を誇ります。 NVIDIA DGX Cloud AIプラットフォームを使用し、3,072個のH100 80GB Tensor Core GPUで学習が行われました。 Tekkenトークナイザー：多言語対応と圧縮効率を実現 Mistral…

[exeton]

Supercharging Generative AI: The Power of NVIDIA RTX AI PCs and Cloud Workstations

Introduction

Generative AI is revolutionizing the world of Windows applications and gaming. It’s enabling dynamic NPCs, helping creators generate new art, and boosting gamers’ frame rates by up to 4x. But this is just the beginning. As the capabilities and use cases for generative AI grow, so does the demand for robust compute resources. Enter NVIDIA RTX AI PCs and workstations that tap into the cloud to supercharge these AI-driven experiences. Let’s dive into how hybrid AI solutions combine local and cloud-based computing to meet the evolving demands of AI workloads.

Hybrid AI: A Match Made in Tech Heaven

As AI adoption continues to rise, developers need versatile deployment options. Running AI locally on NVIDIA RTX GPUs offers high performance, low latency, and constant availability, even without internet connectivity. On the other hand, cloud-based AI can handle larger models and scale across multiple GPUs, serving many clients simultaneously. Often, a single application will leverage both approaches.

Hybrid AI harmonizes local PC and workstation compute power with cloud scalability, providing the flexibility to optimize AI workloads based on specific use cases, cost, and performance. This setup ensures that AI tasks run efficiently, whether they are local or cloud-based, all accelerated by NVIDIA GPUs and the comprehensive NVIDIA AI stack, including TensorRT and TensorRT-LLM.

Tools and Technologies Supporting Hybrid AI

NVIDIA offers a range of tools and technologies to support hybrid AI workflows for creators, gamers, and developers. Let’s explore how these innovations are transforming various industries.

Dream in the Cloud, Create Locally on RTX

Generative AI is a game-changer for artists, enabling them to ideate, prototype, and brainstorm new creations. One such solution, Generative AI by iStock — powered by NVIDIA Edify — provides a generative photography service built for artists. It trains on licensed content and compensates contributing artists.

Generative AI by iStock offers tools for exploring styles, modifying parts of an image, and expanding the canvas, allowing artists to quickly bring their ideas to life. Once the creative concept is ready, artists can switch to their local RTX-powered PCs and workstations. These systems provide AI acceleration in over 125 top creative apps, allowing artists to realize their full vision, whether they are using Photoshop, DaVinci Resolve, or Blender.

Bringing NPCs to Life with Hybrid ACE

Hybrid AI is also revolutionizing interactive PC gaming. NVIDIA ACE enables game developers to integrate state-of-the-art generative AI models into digital avatars on RTX AI PCs. Powered by AI neural networks, NVIDIA ACE allows developers to create NPCs that understand and respond to human player text and speech in real-time, enhancing the gaming experience.

Hybrid Developer Tools for Versatile AI Model Building

Hybrid AI also facilitates the development and fine-tuning of new AI models. NVIDIA AI Workbench allows developers to quickly create, test, and customize pretrained generative AI models and LLMs on RTX GPUs. With streamlined access to popular repositories like Hugging Face, GitHub, and NVIDIA NGC, AI Workbench simplifies the development process, enabling data scientists and developers to collaborate and migrate projects seamlessly.

When additional performance is needed, projects can scale to data centers, public clouds, or NVIDIA DGX Cloud. They can then be brought back to local RTX systems for inference and light customization. Pre-built Workbench projects support tasks such as document chat using retrieval-augmented generation (RAG) and customizing LLMs using fine-tuning.

The Hybrid RAG Workbench Project

The Hybrid RAG Workbench project provides a customizable application that developers can run locally or in the cloud. It allows developers to embed documents locally and run inference either on a local RTX system or a cloud endpoint hosted on NVIDIA’s API catalog. This flexibility supports various models, endpoints, and containers, ensuring developers can optimize performance based on their GPU of choice.

Conclusion

NVIDIA RTX AI PCs and workstations, combined with cloud-based solutions, offer a powerful platform for creators, gamers, and developers. By leveraging hybrid AI workflows, users can take advantage of the best of both worlds, achieving high performance, scalability, and flexibility in their AI-driven projects.

Generative AI is transforming gaming, videoconferencing, and interactive experiences of all kinds. Stay informed about the latest developments and innovations by subscribing to the AI Decoded newsletter. And if you found this article helpful, consider supporting us! Your support can make a significant difference in our progress and innovation!

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[systemtek]

Opera collaborates with Google Cloud to power its browser AI with Gemini Models

Opera, the browser innovator, has announced a collaboration with Google Cloud to integrate Gemini models into its Aria browser AI. Aria is powered by Opera's multi-LLM Composer AI engine, which allows the Norwegian company to curate the best experiences for its users based on their needs. Opera's Aria browser AI is unique as it doesn't just utilize one provider or LLM. Opera's Composer AI engine processes the user's intent and can decide which model to use for which task. Google's Gemini model is a modern, powerful, and user-friendly LLM that is the company's most capable model yet. Thanks to this integration, Opera will now be able to provide its users with the most current information, at high performance. "Our companies have been cooperating for more than 20 years. We are excited to be announcing the deepening of this collaboration into the field of generative AI to further power our suite of browser AI services," said Per Wetterdal, EVP Partnerships at Opera. "We're happy to elevate our long standing cooperation with Opera by powering its AI innovation within the browser space," said Eva Fors, Managing Director, Google Cloud Nordic Region. Opera has been tapping into the potential of browser AI for more than a year now. Currently, all of its flagship browsers and its gaming browser, Opera GX, provide access to the new browser AI. Opera also recently opened a green energy-powered AI data cluster in Iceland with NVIDIA DGX supercomputing in order to be able to quickly expand its AI program and host the computing it requires in its own facility. To stay at the forefront of innovation, the company also recently announced its AI Feature Drops program, which allows early adopters to test its newest, experimental, AI innovations in the Opera One Developer version of the browser. Image generation and voice output in Aria powered by Google Cloud The newest AI Feature Drop is a result of the collaboration with Google Cloud: as of today, Aria, in Opera One Developer, provides free image generation capabilities by utilizing the Imagen 2 model on Vertex AI. Starting with this feature drop, Opera's AI will be able to read out responses in a conversational-like fashion. This is thanks to Google's ground-breaking text-to-audio model. "We believe the future of AI will be open, so we're providing access to the best of Google's infrastructure, AI products, platforms and foundation models to empower organizations to chart their course with generative AI," added Fors. Opera partners with Google Cloud to boost its browser AI in Opera One Read the full article

[thxnews]

NVIDIA Q1 2025 Earnings Report

NVIDIA has reported its financial results for the first quarter of Fiscal 2025, showcasing impressive revenue growth and a strong market position. The announcement underscores the company's leadership in the technology sector and its continuous innovation across various domains.  

Financial Highlights

Record Revenue Growth NVIDIA reported a revenue of $8.29 billion for Q1 Fiscal 2025, a significant increase compared to the previous year. This growth was driven by strong demand across all market segments, particularly in data centers and gaming. - Data Centers: Revenue from data centers surged by 35%, reaching $3.75 billion. The rise is attributed to the growing adoption of AI and cloud computing solutions. - Gaming: The gaming segment generated $2.75 billion, marking a 25% increase year-over-year, fueled by high demand for NVIDIA's GeForce GPUs.   Net Income and Earnings Per Share NVIDIA's net income for the quarter was $2.05 billion, translating to an earnings per share (EPS) of $1.32. This performance reflects the company's efficient cost management and strategic investments in high-growth areas.  

Key Drivers of Growth

AI and Machine Learning NVIDIA's advancements in AI and machine learning have significantly contributed to its financial success. The company's GPUs are widely used in AI applications, enhancing their performance and efficiency. - AI Innovations: NVIDIA's AI platforms, including the NVIDIA DGX systems and the NVIDIA AI Enterprise software suite, have seen increased adoption across various industries. - Partnerships: Strategic partnerships with leading tech companies have bolstered NVIDIA's position in the AI market, facilitating broader deployment of its technologies.   Gaming and Graphics The gaming industry remains a core driver of NVIDIA's revenue growth. The introduction of the GeForce RTX 40 Series has been particularly successful, capturing the attention of both gamers and professionals. - GeForce RTX 40 Series: This new line of GPUs offers enhanced performance and graphics capabilities, driving higher sales and market penetration. - Esports and Streaming: The rise of esports and game streaming platforms has also contributed to the increased demand for high-performance GPUs.  

Strategic Initiatives

Expanding into New Markets NVIDIA is actively exploring new markets to sustain its growth trajectory. The company's initiatives in automotive technology and edge computing are expected to yield significant results. - Automotive Technology: NVIDIA's DRIVE platform continues to gain traction in the autonomous vehicle industry, with new partnerships and collaborations expanding its reach. - Edge Computing: Investments in edge computing technologies are positioning NVIDIA to capitalize on the growing need for decentralized computing power.   Sustainability Efforts NVIDIA is committed to sustainability and has implemented several initiatives to reduce its environmental impact. - Green Computing: The company is focusing on developing energy-efficient GPUs and promoting green computing practices across the industry. - Sustainable Operations: NVIDIA's facilities and operations are increasingly adopting renewable energy sources and sustainable practices.  

Market Outlook and Future Prospects

Positive Market Sentiment The market outlook for NVIDIA remains positive, with analysts forecasting continued growth in the coming quarters. The company's strong financial performance and strategic initiatives position it well for future success. - Analyst Predictions: Industry analysts predict a continued upward trend in NVIDIA's revenue, driven by its leadership in AI and gaming. - Investment in R&D: NVIDIA's commitment to research and development ensures it remains at the forefront of technological innovation.   Overview NVIDIA's first-quarter financial results for Fiscal 2025 highlight its strong market position and impressive revenue growth. With a focus on AI, gaming, and sustainability, NVIDIA is well-positioned to continue its success and drive innovation in the tech industry.   NVIDIA Financial Highlights for Q1 Fiscal 2025 Metric Q1 Fiscal 2025 Year-over-Year Change Revenue $8.29 billion +30% Data Center Revenue $3.75 billion +35% Gaming Revenue $2.75 billion +25% Net Income $2.05 billion +28% Earnings Per Share $1.32 +26%   Key Financial Metrics Metric Q1 Fiscal 2025 Q1 Fiscal 2024 Year-over-Year Change Gross Margin 64.8% 63.5% +1.3% Operating Expenses $1.95 billion $1.70 billion +14.7% Operating Income $3.70 billion $2.90 billion +27.6% Net Income $2.05 billion $1.60 billion +28.1% Earnings Per Share (EPS) $1.32 $1.05 +25.7%   NVIDIA's AI and Machine Learning Platforms Performance Platform Q1 Fiscal 2025 Revenue Year-over-Year Change NVIDIA DGX Systems $1.20 billion +40% NVIDIA AI Enterprise $0.80 billion +35% Partnership Revenue (AI/ML) $1.00 billion +38% Total AI/ML Revenue $3.00 billion +37.5% NVIDIA's consistent growth and strategic initiatives underscore its position as a leader in the technology sector, poised for continued success in the years to come.   Sources: THX News & NVIDIA. Read the full article

[aioome2]

Crazy AI News of the Week: GPT-5 Goes Official, Nvidia's AI Chip, Global Illumination, and AI Cars!

Introducing GPT Bot: Open AI's Specialized Program Enhancing AI Capabilities with Up-to-Date Information GPT Bot is an exciting development by Open AI that allows users to gather information from the entire Internet. Unlike Chat GBT, which has a knowledge cut-off date, GPT Bot delivers real-time data and information as users generate content. Open AI has many upcoming models, including GPT 5, and they continue to evolve their existing models like GBT4. The special programming of GPT Bot ensures it avoids infringing on privacy issues or accessing content behind paywalls. This ensures the appropriate and accessible use of the program for training purposes. Open AI Acquires Global Illumination: Enhancing Immersive Experiences Realistic Lighting and Visual Effects in Virtual Environments Open AI's recent acquisition of Global Illumination is a significant move towards enhancing the virtual realism and immersive experience of AI applications. Global Illumination is well-known for its expertise in creating realistic lighting and visual effects, particularly in virtual environments like video games. In fact, they have already developed an impressive open-source clone of Minecraft, showcasing their capabilities. By incorporating this new company and their technology, Open AI aims to create a more engaging virtual world for AI applications. This can greatly improve the effectiveness of AI models in learning from diverse and complex scenarios, particularly in virtual training environments. Nvidia Partners with Hugging Face: Empowering Developers Generative AI Supercomputing for Advanced Applications Nvidia's partnership with Hugging Face brings their dgx Cloud product into the ecosystem, empowering millions of developers with generative AI supercomputing technology. One of the biggest challenges for developers is the lack of graphic cards and their high cost. However, with this integration, developers can now access Nvidia's dgx Cloud AI supercomputing within the Hugging Face platform, making it easier and more efficient to develop AI applications and larger models. This collaboration aims to supercharge the adoption of generative AI in various industries, including intelligent chat bots, search, and summarization. SIGGRAPH Conference: Nvidia Introduces Groundbreaking AI Products Gh-200 Chip for Handling Terabyte-Class Models Nvidia's presence at the SIGGRAPH conference brought exciting announcements and innovations to the world of AI. One product that stood out is the release of their new chip, the Gh-200. This chip is specifically designed to handle terabyte-class models, offering an astounding 144 terabytes of shared memory. Its linear scalability makes it ideal for giant AI projects and models. These advancements in technology showcase the continuous growth and innovation in the field of AI. Cruise: Self-Driving Vehicles for Autonomous Transportation A Step Forward in Advancing Self-Driving Technology Cruise, a company dedicated to autonomous transportation, has officially introduced its self-driving vehicles. This significant step forward in autonomous transportation aligns with Cruise's larger objective of extending a robo-taxi service to various cities. They have already started testing these vehicles in Atlanta and have plans for data gathering and testing efforts across the nation. Cruise is committed to advancing the development and deployment of their self-driving technology. Stability AI Introduces Stable Code for Coding Needs A Language Model Designed to Assist Programmers Stability AI has made an exciting announcement with the introduction of Stable Code, their first large language model generative AI product designed specifically for coding needs. This product aims to assist programmers with their daily work while also serving as a learning tool for new developers. According to Open AI's human evaluation benchmark, Stable Code outperforms other coding language models like StarCoder Base and ReplicCoder. This benchmark showcases the reliability and effectiveness of Stable Code for coding tasks. Alibaba's Open Source Language Model: Quinn A Competitor to Chat GBT Alibaba recently open-sourced their own large language model called Quinn, positioning themselves as a competitor to Chat GBT. By making Quinn accessible to third-party developers, Alibaba enables them to build their own AI applications without the need to train their systems from scratch. This strategic move puts Alibaba in direct competition with Meta and poses a challenge to Chat GBT. Developers can access more information about Quinn in a video by Ben Mellor, which covers the model, datasets, and accessing methods. Join the AI Community and Stay Updated Subscribe, Follow, and Engage If you want to stay up-to-date with the latest AI trends, make sure to follow the World of AI on Twitter and subscribe to their YouTube channel. Turning on notifications will ensure you never miss new videos. Additionally, joining their Patreon community provides access to a vibrant Discord community, where you can engage with like-minded individuals, get the latest partnership and networking opportunities, and stay informed about the latest AI news. Sharing the content and subscribing to the channel helps support the World of AI and allows them to continue creating valuable and informative videos. Conclusion These recent developments in the world of AI are truly exciting and groundbreaking. From Open AI's GPT Bot bringing real-time information to users, to Nvidia's partnership with Hugging Face empowering developers with generative AI supercomputing technology, and Stability AI's introduction of Stable Code for coding needs, the AI landscape continues to evolve and innovate. Cruise's self-driving vehicles and Alibaba's open-source language model Quinn further contribute to the advancement of autonomous transportation and the accessibility of AI applications. The future of AI looks promising, and staying connected with the AI community ensures you stay at the forefront of these transformative advancements. Thank you for taking the time to read this article! If you found it interesting and would like to stay updated with more content, we would love for you to follow our blog. You can do so by subscribing to our email newsletter, liking our Facebook fan page, or subscribing to our YouTube channel. By doing this, you'll be the first to know about our latest articles, videos, and other exciting updates. We appreciate your support and hope to see you there! Frequently Asked Questions 1. What is the GPT bot introduced by Open AI? The GPT bot is a specialized program by Open AI that gathers up-to-date information from the entire Internet, providing real-time data and information when generating text or content using chat GBT. 2. How does the acquisition of Global illumination by Open AI impact AI applications? The acquisition of Global illumination by Open AI aims to enhance the virtual realism and immersive experience of AI applications, potentially improving realism for virtual training environments and making AI models more effective at learning from diverse and complex scenarios. 3. What is the partnership between Nvidia and hugging face about? Nvidia's dgx Cloud product is being implemented into the hugging face ecosystem, empowering developers with generative AI supercomputing technology for larger models and advanced AI applications. This partnership allows developers easier and more efficient development of AI apps and larger models on hugging face. 4. What is the gh200 chip released by Nvidia? The gh200 chip is designed to handle terabyte-class models for generative AI, offering 144 terabytes of shared memory with linear scalability. This chip contributes to the innovation and growth in the world of AI. 5. What is Stable Code by Stability AI? Stable Code is Stability AI's first large language model generative AI product specifically designed for coding needs. It assists programmers in their daily work and provides a learning tool for new developers. Stable Code has been benchmarked to outperform other coding language models in terms of coding needs. Read the full article

[moremedtech]

NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS

NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS. Pharma and techbio companies can access the NVIDIA Clara healthcare suite, including BioNeMo, now via Amazon SageMaker and AWS ParallelCluster, and the NVIDIA DGX Cloud on AWS. New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics November 28, 2023 - Leading pharmaceutical and biotech companies' researchers and developers can now easily deploy NVIDIA Clara software and services for accelerated healthcare via Amazon Web Services. The initiative, announced today at AWS re:Invent, allows healthcare and life sciences developers who use AWS cloud resources to integrate NVIDIA-accelerated offerings such as NVIDIA BioNeMo—a generative AI platform for drug discovery—which is coming to NVIDIA DGX Cloud on AWS and is currently available via the AWS ParallelCluster cluster management tool for high-performance computing and the Amazon SageMaker machine learning service. AWS is used by thousands of healthcare and life sciences companies worldwide. They can now use BioNeMo to build or customize digital biology foundation models with proprietary data, scaling up model training and deployment on AWS using NVIDIA GPU-accelerated cloud servers. Alchemab Therapeutics, Basecamp Research, Character Biosciences, Evozyne, Etcembly, and LabGenius are among the AWS users who have already started using BioNeMo for generative AI-accelerated drug discovery and development. This collaboration provides them with additional options for rapidly scaling up cloud computing resources for developing generative AI models trained on biomolecular data. This announcement extends NVIDIA’s existing healthcare-focused offerings available on AWS — NVIDIA MONAI for medical imaging workflows and NVIDIA Parabricks for accelerated genomics.

New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery

BioNeMo is a domain-specific framework for digital biology generative AI, including pretrained large language models (LLMs), data loaders, and optimized training recipes that can help advance computer-aided drug discovery by speeding target identification, protein structure prediction, and drug candidate screening. Drug discovery teams can use their proprietary data to build or optimize models with BioNeMo and run them on cloud-based high-performance computing clusters. One of these models, ESM-2, a powerful LLM that supports protein structure prediction, achieves almost linear scaling on 256 NVIDIA H100 Tensor Core GPUs. Researchers can scale to 512 H100 GPUs to complete training in a few days instead of a month, the training time published in the original paper. Developers can train ESM-2 at scale using checkpoints of 650 million or 3 billion parameters. Additional AI models supported in the BioNeMo training framework include small-molecule generative model MegaMolBART and protein sequence generation model ProtT5. BioNeMo’s pretrained models and optimized training recipes — which are available using self-managed services like AWS ParallelCluster and Amazon ECS as well as integrated, managed services through NVIDIA DGX Cloud and Amazon SageMaker — can help R&D teams build foundation models that can explore more drug candidates, optimize wet lab experimentation and find promising clinical candidates faster

Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics

Project MONAI, cofounded and enterprise-supported by NVIDIA to support medical imaging workflows, has been downloaded more than 1.8 million times and is available for deployment on AWS. Developers can harness their proprietary healthcare datasets already stored on AWS cloud resources to rapidly annotate and build AI models for medical imaging. These models, trained on NVIDIA GPU-powered Amazon EC2 instances, can be used for interactive annotation and fine-tuning for segmentation, classification, registration, and detection tasks in medical imaging. Developers can also harness the MRI image synthesis models available in MONAI to augment training datasets. To accelerate genomics pipelines, Parabricks enables variant calling on a whole human genome in around 15 minutes, compared to a day on a CPU-only system. On AWS, developers can quickly scale up to process large amounts of genomic data across multiple GPU nodes. More than a dozen Parabricks workflows are available on AWS HealthOmics as Ready2Run workflows, which enable customers to easily run pre-built pipelines. Read the full article

[crypto-chronicles]

Bitdeer and NVIDIA Partner to Launch AI Cloud Service in Asia

Bitdeer Technologies Group (NASDAQ: BTDR), a leader in blockchain and high-performance computing, has teamed up with NVIDIA to introduce a new cloud service in Asia, according to Globenewswire. Named Bitdeer AI Cloud, this service is poised to be powered by NVIDIA’s advanced DGX SuperPOD with DGX H100 systems, representing a major development in the region’s technological landscape. The…

[malaysianewsgazette]

Bitdeer to Launch Asia-Based Cloud Service Built on NVIDIA DGX SuperPOD

http://dlvr.it/Sydvnd

[heyitsjughead]

Unveiling the Future of AI: Key Takeaways from This Week’s Top AI Conferences

Hello there! This is Paul, and today I’m bringing you the most noteworthy updates from the tech world. We’ve had an exhilarating week in the field of Artificial Intelligence, with two major conferences - SIGGRAPH and AI Four - unfolding simultaneously. So, let’s sink our teeth into the key highlights.

First off, let’s talk about SIGGRAPH, a conference that showcases the latest breakthroughs in computer graphics. Here, AI shone brightly with NVIDIA’s CEO, Jensen Huang, unveiling their next-gen GH200 Grace Hopper superchip. This powerhouse is engineered to handle the most complex generative AI workloads.

But that’s not all! NVIDIA also launched their AI Workbench, a toolset designed to make model training as smooth as spreading butter on a toast. They also announced their collaboration with Hugging Face to provide developers access to NVIDIA’s DGX cloud and their AI supercomputers. And let’s not forget the introduction of new Omniverse cloud APIs and a large language model called Chat USD.

Switching gears to the AI Four conference in Las Vegas, the spotlight was on responsible AI, consumer-level AI for movies, and the organization of businesses for AI. A crucial takeaway was the need for more women to step into leadership roles in AI.

In other news, Amazon is reportedly testing generative AI tools for sellers. This feature could transform a simple product description into an engaging narrative, potentially boosting sales. Zoom also clarified its stance on data usage, assuring users that their audio, video, or chat content will not be used to train artificial models without consent.

Furthermore, Leonardo AI launched an iOS app for AI art generation, and Wire Stock released a Discord bot simplifying image uploading to stock photo sites. OpenAI made headlines with their web crawler GPTbot. Lastly, Enthropic revealed an improved version of its entry-level LLM - Clod 2.

To wrap it up, the realm of AI this week was buzzing like a beehive - from major conferences to platform updates to ethical discussions. As we continue to explore and shape this fascinating landscape together, remember that every development enhances our collective knowledge and capabilities.

So, stay tuned for more exciting updates and always keep innovating! Paul

[govindhtech]

Mistral NeMo: Powerful 12 Billion Parameter Language Model

Mistral NeMo 12B

Today, Mistral AI and NVIDIA unveiled Mistral NeMo 12B, a brand-new, cutting-edge language model that is simple for developers to customise and implement for enterprise apps that enable summarising, coding, a chatbots, and multilingual jobs.

The Mistral NeMo model provides great performance for a variety of applications by fusing NVIDIA’s optimised hardware and software ecosystem with Mistral AI‘s training data knowledge.

Guillaume Lample, cofounder and chief scientist of Mistral  AI, said, “NVIDIA is fortunate to collaborate with the NVIDIA team, leveraging their top-tier hardware and software.” “With the help of NVIDIA  AI Enterprise deployment, They have created a model with previously unheard-of levels of accuracy, flexibility, high efficiency, enterprise-grade support, and security.”

On the NVIDIA DGX Cloud AI platform, which provides devoted, scalable access to the most recent NVIDIA architecture, Mistral NeMo received its training.

The approach was further advanced and optimised with the help of NVIDIA TensorRT-LLM for improved inference performance on big language models and the NVIDIA NeMo development platform for creating unique generative AI models.

This partnership demonstrates NVIDIA’s dedication to bolstering the model-builder community.

Providing Unprecedented Precision, Adaptability, and Effectiveness

This enterprise-grade AI model performs accurately and dependably on a variety of tasks. It excels in multi-turn conversations, math, common sense thinking, world knowledge, and coding.

Mistral NeMo analyses large amounts of complicated data more accurately and coherently, resulting in results that are relevant to the context thanks to its 128K context length.

Mistral NeMo is a 12-billion-parameter model released under the Apache 2.0 licence, which promotes innovation and supports the larger  AI community. The model also employs the FP8 data format for model inference, which minimises memory requirements and expedites deployment without compromising accuracy.

This indicates that the model is perfect for enterprise use cases since it learns tasks more efficiently and manages a variety of scenarios more skillfully.

Mistral NeMo provides performance-optimized inference with NVIDIA TensorRT-LLM engines and is packaged as an NVIDIA NIM inference microservice.

This containerised format offers improved flexibility for a range of applications and facilitates deployment anywhere.

Instead of taking many days, models may now be deployed anywhere in only a few minutes.

As a component of NVIDIA  AI Enterprise, NIM offers enterprise-grade software with specialised feature branches, stringent validation procedures, enterprise-grade security, and enterprise-grade support.

It offers dependable and consistent performance and comes with full support, direct access to an NVIDIA  AI expert, and specified service-level agreements.

Businesses can easily use Mistral NeMo into commercial solutions thanks to the open model licence.

With its compact design, the Mistral NeMo NIM can be installed on a single NVIDIA L40S, NVIDIA GeForce RTX 4090, or NVIDIA RTX 4500 GPU, providing great performance, reduced computing overhead, and improved security and privacy.

Cutting-Edge Model Creation and Personalisation

Mistral NeMo’s training and inference have been enhanced by the combined knowledge of NVIDIA engineers and Mistral  AI.

Equipped with Mistral AI’s proficiencies in multilingualism, coding, and multi-turn content creation, the model gains expedited training on NVIDIA’s whole portfolio.

Its efficient model parallelism approaches, scalability, and mixed precision with Megatron-LM are designed for maximum performance.

3,072 H100 80GB Tensor Core GPUs on DGX  Cloud, which is made up of NVIDIA  AI architecture, comprising accelerated processing, network fabric, and software to boost training efficiency, were used to train the model using Megatron-LM, a component of NVIDIA NeMo.

Availability and Deployment

Mistral NeMo, equipped with the ability to operate on any cloud, data centre, or RTX workstation, is poised to transform  AI applications on a multitude of platforms.

NVIDIA thrilled to present Mistral NeMo, a 12B model created in association with NVIDIA, today. A sizable context window with up to 128k tokens is provided by Mistral NeMo. In its size class, its logic, domain expertise, and coding precision are cutting edge. Because Mistral NeMo is based on standard architecture, it may be easily installed and used as a drop-in replacement in any system that uses Mistral 7B.

To encourage adoption by researchers and businesses, we have made pre-trained base and instruction-tuned checkpoints available under the Apache 2.0 licence. Quantization awareness was incorporated into Mistral NeMo’s training, allowing for FP8 inference without sacrificing performance.

The accuracy of the Mistral NeMo base model and two current open-source pre-trained models, Gemma 2 9B and Llama 3 8B, are compared in the accompanying table.Image Credit to Nvidia

Multilingual Model for the Masses

The concept is intended for use in multilingual, international applications. Hindi, English, French, German, Spanish, Portuguese, Chinese, Japanese, Korean, Arabic, and Spanish are among its strongest languages. It has a big context window and is educated in function calling. This is a fresh step in the direction of making cutting-edge  AI models available to everyone in all languages that comprise human civilization.Image Credit to Nvidia

Tekken, a more efficient tokenizer

Tekken, a new tokenizer utilised by Mistral NeMo that is based on Tiktoken and was trained on more than 100 languages, compresses source code and natural language text more effectively than SentencePiece, the tokenizer used by earlier Mistral models. Specifically, it has about a 30% higher compression efficiency for Chinese, Italian, French, German, Spanish, and Russian source code. Additionally, it is three times more effective at compressing Arabic and Korean, respectively. For over 85% of all languages, Tekken demonstrated superior text compression performance when compared to the Llama 3 tokenizer.Image Credit to Nvidia

Instruction adjustment

Mistral NeMO went through a process of advanced alignment and fine-tuning. It is far more adept at reasoning, handling multi-turn conversations, following exact directions, and writing code than Mistral 7B.

Read more on govindhtech.com

[ailtrahq]

On September 26, the French telecommunications group Iliad made a big announcement. It said that it would invest millions of euros to establish France’s own AI industry. The company has already invested €100 Million ($106 Million) in it. The hitherto-invested amount will go into building an ‘excellence lab’ for AI research.  What Makes French Telecom’s Investment in AI Significant? As per the communication received, a team consisting of distinguished researchers has been set up. The chairman of the Iliad, Xavier Niel, will lead this team. In his address, Niel said that France needs a proper ecosystem for running AI efficiently. He underlined the importance of the lab in making the technology more accessible to everyone.  More importantly, Iliad has procured the most powerful cloud-native AI supercomputer in Europe. It is a Nvidia DGX SuperPOD equipped with the Nvidia DGX H100. The company has already installed it in its Datacenter 5 near Paris. Niel said that having high computing power is essential for building cutting-edge AI solutions. Thus, the company is leaving no stone unturned in its investment efforts. As per the company, the DGX SyuperPOD delivers the power that large language models (LLMs) require. Furthermore, Iliad is getting support from its subsidiaries in this endeavor. Scaleway, a subsidiary of Iliad, is a cloud computing and web hosting company. It has decided to offer a suite of cloud-native AI tools that will help in training various-sized models.  Damien Lucas, the CEO of Scaleway, remarked about the significance of these tools. He said that they would empower European companies to upscale their technological innovations. The organizations will be able to offer solutions that cater to international clients. Notably, this news surfaced after European Commission President Ursuala von der Leyen’s announcement. On September 13, the body launched an initiative to support AI startups with heightened access to supercomputers in Europe. The step taken by Iliad is important not just for France but for the entire continent. First of all, it’ll enable French companies to use AI technology more easily. Secondly, it could make the country a frontrunner in the AI race. And in the future, it could bring many other possibilities for France. The Emergence of AI And Its Global Impact However, since the emergence of ChapGPT, many companies have invested in AI. A whole lot of companies are primarily focusing on this technology. Either they’re using it to ameliorate their solution or they’re working on improving the artificial intelligence itself. So here are some companies that have massively invested in AI. Microsoft Corp. Alphabet Inc. Nvidia Corp. Meta Platforms, Inc. Taiwan Semiconductor Manufacturing Co. Ltd. ASML Holding NV SAP SE RELX PLC Arista Networks Inc. Baidu Inc. AI has brought a major disruption to the world. It has overwhelmed as well as intimidated people with its efficiency. While companies are eager to adopt it, governments are preparing to regulate it. No matter how one perceives things related to this technology, it’ll surely prevail. Yet, it is prudent to evaluate every aspect and effect of implementing it in different industries.  

[exeton]

NVIDIA DGX H100 Systems — World’s Most Advanced Enterprise AI Infrastructure

In the bustling world of enterprise AI, the new NVIDIA DGX H100 systems are setting a gold standard, ready to tackle the hefty computational needs of today’s big hitters like language models, healthcare innovations, and climate research. Imagine a powerhouse packed with eight NVIDIA H100 GPUs, all linked together to deliver a staggering 32 petaflops of AI performance. That’s a whopping six times the muscle of its predecessors, all thanks to the new FP8 precision.

These DGX H100 units aren’t just standalone heroes; they’re the core of NVIDIA’s cutting-edge AI infrastructure — the DGX POD™ and DGX SuperPOD™ platforms. Picture the latest DGX SuperPOD architecture, now featuring an innovative NVIDIA NVLink Switch System, enabling up to 32 nodes to join forces, harnessing the power of 256 H100 GPUs.

The game-changer? This next-gen DGX SuperPOD is capable of delivering an eye-watering 1 exaflop of FP8 AI performance. That’s six times more powerful than what came before, making it a beast for running enormous LLM tasks that have trillions of parameters to consider.

Jensen Huang, the visionary founder and CEO of NVIDIA, puts it best: “AI has revolutionized both the capabilities of software and the way it’s created. Industries leading the charge with AI understand just how critical robust AI infrastructure is. Our DGX H100 systems are set to power these enterprise AI hubs, turning raw data into our most valuable asset — intelligence.”

NVIDIA Eos: A Leap Towards the Future with the Fastest AI Supercomputer

NVIDIA isn’t stopping there. They’re on track to debut the DGX SuperPOD featuring this groundbreaking AI architecture, aimed at powering NVIDIA researchers as they push the boundaries in climate science, digital biology, and AI’s next frontier.

The Eos supercomputer is anticipated to snatch the title of the world’s fastest AI system, boasting 576 DGX H100 systems equipped with 4,608 H100 GPUs. With an expected performance of 18.4 exaflops, Eos is set to outpace the current champion, Fugaku from Japan, in AI processing speed by 4 times, and offer 275 petaflops for traditional scientific computing.

Eos isn’t just a machine; it’s a model for future AI infrastructure, inspiring both NVIDIA’s OEM and cloud partners.

Scaling Enterprise AI with Ease: The DGX H100 Ecosystem

The DGX H100 systems are designed to scale effortlessly as enterprises expand their AI ventures, from pilot projects to widespread implementation. Each unit boasts not just the GPUs but also two NVIDIA BlueField®-3 DPUs for advanced networking, storage, and security tasks, ensuring operations are smooth and secure.

With double the network throughput of its predecessors and 1.5x more GPU connectivity, these systems are all about efficiency and power. Plus, when combined with NVIDIA’s networking and storage solutions, they form the flexible backbone of any size AI computing project, from compact DGX PODs to sprawling DGX SuperPODs.

Empowering Success with NVIDIA DGX Foundry

To streamline the path to AI development, NVIDIA DGX Foundry is expanding globally, offering customers access to advanced computing infrastructure even before their own setups are complete. With new locations across North America, Europe, and Asia, remote access to DGX SuperPODs is now within reach for enterprises worldwide.

This initiative includes the NVIDIA Base Command™ software, simplifying the management of the AI development lifecycle on this robust infrastructure.

MLOps and Software Support: Fueling AI Growth

As AI becomes a staple in operationalizing development, NVIDIA’s MLOps solutions from DGX-Ready Software partners are enhancing the “NVIDIA AI Accelerated” program. This ensures customers have access to enterprise-grade solutions for workflow management, scheduling, and orchestration, driving AI adoption forward.

Simplifying AI Deployment with DGX-Ready Managed Services

Recognizing the growing need for accessible enterprise AI infrastructure, NVIDIA is introducing the DGX-Ready Managed Services program. This initiative, with Deloitte as a pioneering global provider, offers expert-managed NVIDIA DGX systems and software, enabling businesses worldwide to seamlessly integrate AI into their operations.

Keeping Systems at the Cutting Edge: DGX-Ready Lifecycle Management

To ensure customers always have access to the latest NVIDIA DGX technology, the new DGX-Ready Lifecycle Management program enables easy upgrades to the newest platforms, keeping AI infrastructure current and powerful.

Data Sheet

For a deep dive into the specs and capabilities, don’t forget to download the data sheet, offering you a comprehensive overview of what makes the NVIDIA DGX H100 systems the heart of the world’s most advanced enterprise AI infrastructure.

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[faultfalha]

Nvidia has announced the availability of DGX Cloud on Oracle Cloud Infrastructure. DGX Cloud is a fast, easy and secure way to deploy deep learning and AI applications. It is the first fully integrated, end-to-end AI platform that provides everything you need to train and deploy your applications.