NEC Professor of Software Science and Engineering Muriel Medard discusses Wireless APIs at the Edge.
Author: maribellopez
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HPE Dives Into Supercomputing As-A-Service With GreenLake For AI LLMs
[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row column_structure=”2_5,3_5″ _builder_version=”4.5.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”2_5″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https://lopezresearch.com/wp-content/uploads/2026/07/6099f-tech-19.jpg” title_text=”tech-19″ _builder_version=”4.5.1″ _module_preset=”default”][/et_pb_image][/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.5.1″ background_enable_image=”off” custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.5.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”]Last week at Hewlett Packard Enterprise (HPE) Discover, the company announced its entry into the AI cloud market by expanding its HPE GreenLake portfolio. In 2019, HPE acquired supercomputing vendor Cray Inc.
At that time, Antonio Neri, the CEO of HPE, said, “Answers to some of society’s most pressing challenges are buried in massive amounts of data.” Yet supercomputing was reserved mainly for academic researchers or companies with deep pockets. The need for more advanced computing infrastructures grew exponentially as companies began adopting AI.
While cloud computing services hold the promise of infinite bandwidth, these services aren’t always the most efficient or cost-effective way to train large-scale models. The new HPE AI cloud service, called HPE GreenLake for large language models (LLMs), enables organizations to train, tune, and deploy AI models at scale and with security and compliance to foster responsible and sustainable AI adoption.
[/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”4.5.1″ _module_preset=”default”][et_pb_column type=”4_4″ _builder_version=”4.5.1″ _module_preset=”default”][et_pb_text _builder_version=”4.5.1″ _module_preset=”default” background_color=”#ffffff”]HPE GreenLake for LLMs runs on HPE Cray XD supercomputers. This new GreenLake offering eliminates the need for customers to purchase and manage supercomputers, which can be costly and complex. The offering leverages the HPE Cray Programming Environment and HPE’s AI and ML software suite, providing comprehensive tools for optimizing HPC and AI applications, training large-scale models, and managing data.
[/et_pb_text][et_pb_text _builder_version=”4.5.1″ _module_preset=”default”]HPE teamed up with Aleph Alpha, a German AI startup, to offer ready-to-use large language models (LLMs) as part of its on-demand, multi-tenant supercomputing cloud service. HPE GreenLake for LLMs offers access to Aleph Alpha’s 13 billion parameters pre-trained large language model, Luminous, available in multiple languages. HPE GreenLake for LLMs enables enterprises to use their company data to train, tune, and deploy large-scale AI models using HPE’s AI software and supercomputers. By using a company’s databases, articles, and industry-specific data, businesses can ground the model to factual data and relevant user context to eliminate issues such as hallucinations. A hallucination in artificial intelligence (also called confabulation or delusion) is a confident response by an AI that is incorrect or does not seem to be justified by its training data.
HPE didn’t announce any specific support for OpenAI, but it’s clear that OpenAI is deeply involved with Microsoft. HPE’s offering provides another option in the market if a company is looking for an alternative to Microsoft or OpenAI. What does it mean for companies? One of the common challenges with training LLM is that they may not run or will stall during operation due to a lack of computing power. Insufficient computing power slows down the time it takes to train the model while increasing the cost of a model because of restarts.
Companies need a way to run HPC-focused machine learning applications without building and managing on-premises supercomputers. As noted above, another issue with using open-source LLMs today is that they are generic models trained on a large corpus of Internet data that may be inaccurate and lack specific industry data. Supercomputing for LLMs can address different sectors’ unique requirements and challenges by linking industry data with more generic LLMs. HPE said it plans to launch a series of industry and domain-specific AI applications in the future.
These applications will cater to various sectors, including climate modeling, healthcare and life sciences, financial services, manufacturing, and transportation. It makes sense to address these verticals because each industry has vast volumes of specific data. These industries also have use cases where the financial benefits of successful outcomes would far outweigh the cost of AI supercomputing.
Speed is another area where AI supercomputing provides interest. For example, detecting fraud and security threats in financial services transactions requires a combination of high data volume processing in compressed timeframes. You may be asking why you can’t use general-purpose cloud offerings for AI. The answer is you can for specific workloads, but not AI workloads are the same. HPC workloads require an AI-native architecture specifically designed to handle a single large-scale AI training and simulation workload at full computing capacity. Unlike parallel workloads, HPE GreenLake for LLMs supports AI and high-performance computing jobs on hundreds or thousands of CPUs or GPUs simultaneously. This infrastructure design allows for more effective and efficient AI training, resulting in more accurate models that accelerate problem-solving for enterprises. Now this doesn’t imply the hyperscalers are resting on their laurels.
Google recently announced an A3 supercomputer with Nvidia H100 GPUs, while Amazon recently updated its HPC7g instances. However, HPE’s offering should be particularly appealing to existing HPE GreenLake customers that are building hybrid cloud strategies and have HPC requirements.
GOOGLE CLOUD BLOG
Introducing A3 supercomputers with NVIDIA H100 GPUs | Google Cloud Blog Sustainability is another issue within the AI landscape. Most large enterprises that Lopez Research consults with are defining sustainability metrics, adding ESG monitoring solutions and actively seeking out ways to reduce energy consumption. The processing power required to support new AI models is at odds with these goals in many ways. As a result, businesses are leaning heavily on their infrastructure providers to deliver solutions that help address sustainability concerns. All the hyperscaler cloud companies are designing various infrastructure, cooling and optimization solutions to address this problem. HPE’s also in this camp. It said its GreenLake for LLMs operates in colocation facilities that utilize nearly 100% renewable energy. The HPE GreenLake for LLMs service requires specialized supercomputing data centers optimized for power and cooling, which means HPE GreenLake for LLMs will be available in North America by the end of the calendar year 2023, followed by Europe in early 2024. By partnering with QScale in North America, HPE ensures the service aligns with environmentally friendly practices.
However, not all AI use cases require supercomputing power. If your company doesn’t require supercomputing power but needs servers optimized for AI workloads, HPE updated its ProLiant Gen11 servers with 4th Gen Intel Xeon Scalable CPUs, offering improved performance for AI inference tasks. It’s exciting and surprising to see HPE bring a new as-a-service computing offering to the market during its Discover conference. HPE’s move aims to significantly shift the AI landscape by making HPC more accessible to a broader range of organizations. This type of service appears best suited for larger enterprises with large volumes of data and the money to pay for on-demand supercomputing, but it’s still a leap forward in the market. Even if a large enterprise could afford to build large-scale AI infrastructure, it’s well-known that there is a shortage of NVIDIA GPUs.
You can’t train models if you don’t have the proper hardware. It will be interesting to see what type of companies embrace these new services and what workloads will now be possible with access to increased processing power.
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37: Edge AI & Distributed Application Management
Elevate the Edge hosts Jo and Maribel discuss Edge AI and Distributed App Management with Roshni Bhagalia, Vice President of Product Management — Editon AI & Patflorm for GE Healthcare.
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36: S2 E36: David Dobson of Intel on the Value of AI at the Edge
In this episode, Jo and Maribel have a conversation with David Dobson, Global Industry Director Retail, Hospitality & Consumer Goods for Intel, about the Value of AI at the Edge
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35: 35. Samantha Madrid, Group VP, Security & Business Strategy, Juniper Networks
Join us for a special episode of Elevate the Edge from the Juniper Analyst Summit in Dallas featuring Juniper Networks Group Vice President of Security Business & Strategy Samantha Madrid.
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34: 34. Edge in Financial Services Institutions with Theo Lau, Unconventional Ventures
Guest Bio: Theodora (Theo) Lau is the founder of Unconventional Ventures, a public speaker, and an advisor. She is the co-author of Beyond Good, and host of One Vision, a podcast on fintech and innovation. Her monthly column on FinTech Futures explores the intersection of financial services, tech, and humanity. She is also a regular contributor for top industry events and publications, including BBC News, Finovate, American Banker, and Journal of Digital Banking.
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33: 33. Edge to Cloud Integrations with Keith Townsend
Elevate the Edge welcomes guest Keith Townsend, Founder of CTO Advisor, who discusses Edge to Cloud Integrations.
Guest Bio
Keith Townsend Principal at the CTO Advisor a research and advisory services firm. Keith brings his experience covering 4-decades in IT focused on technologies like infrastructure, storage, virtualization, cloud and edge to the podcast.
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30: Trends in Enterprise Mobility From Samsung’s Head of B2B Mobile
What has change in enterprise mobility. Are new industries adopting mobile? Is 5G hot? In this podcast, Chris Balcik, the Vice President and Head of Mobile B2B at Samsung Electronics America shares his perspectives on changes in the market. Chris Balcik Bio: With over 26 years’ experience, including management consulting with the Department of Defense and other Federal clients, Chris has deep expertise in organizational transformation and the role of technology. Prior to his cur…
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32: S2 E32: The Role of AI at the Edge
Guest Tamara McCleary, Founder and CEO of Thulium, discusses the Role of AI at the Edge and which industry has the potential to see significant advancements.
Guest Bio
Tamara McCleary is the founder and CEO of global digital marketing agency, Thulium. She serves as a technology futurist and an advisor to leading global tech companies Amazon, Alexa, Oracle, SAP, Cisco, Dell, IBM, Mercer, Marsh & McLennan Companies, AT&T, Verizon, Citrix, and RSA Security.
Additionally Tamara is a research scientist and technology ethicist and recent graduate (5/2022) of Harvard University's Kennedy School and continuing dual degree student at the Harvard Divinity School. Her research includes science, technology, artificial general intelligence, and posthumanism.
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29: Bonus Episode: Reimagining File Storage and Data Recovery From Ransomeware Attacks With Nasuni’s David Grant
In this episode, we discuss the differences between cloud file storage and traditional storage systems. We discuss the challenge of performance and distributed data. Dave also shares strategies for how companies should approach data in remote work and recovery issues from ransomware attack Bio: David Grant, President at Nasuni David Grant is responsible for all field operations, which include sales, marketing, alliances, professional services, customer success and experience. Dav…
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31: Edge Cloud Adoption with David Linthicum
Hosts Jo Peterson and Maribel Lopez discuss Edge Cloud adoption with David Linthicum, Chief Cloud Strategy Office, Deloitte Consulting.
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30: Ocean Agriculture, Seaweed and Edge
Elevate the Edge hosts Jo Peterson and Maribel Lopez discuss Seaweed, Ocean Agriculture & Edge with Brian Schmidt, Co-founder of PrimaryOcean.
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29: The Role of AI at the Edge with Andi Huels
Elevate the Edge hosts Jo and Maribel discuss the Role of AI at the Edge with Lenovo's Andi Huels.
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28: 28. Meryem Simsek, Bell Labs discusses What’s Next for Edge Connectivity
Meryem Simsek, Head of Network Architecture Research at Bell Labs joins Elevate the Edge hosts Jo Peterson and Maribel Lopez for a discussion about What's Next for Edge Connectivity.
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28: S2. #28 Mitel’s Nagaswamy Talks About The Changes in Communications for Distributed Work
Unified communications, collaboration and contact center technologies have changed as we’ve moved toward distributed work. In this episode we discuss some of the current and upcoming trends in these markets. Bio Mitel’s Chief Marketing Officer, Venkat Nagaswamy, is a seasoned, highly strategic marketing and demand generation executive with a proven track record of growth and success through building great teams. As Chief Marketing Officer, Nagaswamy is responsible for Mitel’s glo…
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27: Edge to Cloud Architectures and Use Cases
Sarbjeet Johal, Founder & Chief Analyst, Stackpane discusses Edge to Cloud Architectures and use cases with Elevate the Edge hosts.
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24: 24. Intel’s Kavitha Prasad Defines the Types of AI Acceleration and What’s New in AI
In this podcast, I interview Kavitha Prasad. She is Intel's VP for Artificial Intelligence Strategy. We started the conversation by defining the various types of AI accelerators to deliver clarity. We move on to discuss the barriers to unlocking value from AI and advice on how to improve AI implementations. We close the podcast with what's new and exciting in the space.
Her bio.
As an experienced Business and Technology leader across a wide range of system applications, system architecture, business development, and go-to-market strategies, she has delivered competitive solutions to Cloud, Enterprise and Embedded markets.
Kavitha has a masterful track record of building high performance teams that consistently deliver high quality products at an aggressive cadence. As an established leader in the semiconductor industry, Kavitha’s broad hardware and software proficiency enables her to deliver multiple successful products in ASICs, SOCs, FPGAs and servers across multiple process nodes.
Best known for leveraging her passion for building authentic customer relationships and coupling it with her technical engineering expertise, Kavitha brings best-in-class AI and Machine Learning solutions in training and inference across edge to cloud.
You can follow Kavitha here:
https://www.linkedin.com/in/kavitha-prasad-2b38737/ -
27: 3M’s Kathryn Coleman Shares Insights From Its Work Your Way Program
Executives need to define how their organization will support the a mixture of hybrid work, remote work and full-time office workers. In 2021, 3M rolled out a strategy it called Work Your Way. In this conversation with 3M’s Kathryn Coleman, we discuss the lessons the company has learned over that past year as it’s put this strategy into place. You can read more about the strategy here. Bio Dr. Kathryn J. Coleman has over 20 years of experience in talent acquisition, diversity, strate…
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Managing Multi-Cloud Madness
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Vittorio Viarengo VMware
Companies of all sizes embraced cloud computing, with organizations deploying workloads and applications across a mixture of private infrastructure and public clouds. The terms multi-cloud and hybrid cloud are part of today’s technology lexicon. Multi-cloud refers to using multiple cloud computing services from different vendors in a hybrid infrastructure. Instead of relying on a single vendor for all its cloud computing needs, a business may use multiple clouds to access best-of-breed cloud services, diversify infrastructure risk and minimize vendor lock-in.While multi-cloud computing provides impressive benefits, it’s not without challenges. Cloud vendors use different technologies, making it more difficult for IT leaders to standardize on a single set of tools and processes. An increasingly diverse set of infrastructure solutions makes it challenging to manage the performance, security and governance of data and applications that operate across multiple clouds. Constrained IT teams lack the headcount to have a dedicated staff to manage the various clouds.
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The 2022 Lopez Research enterprise benchmark survey revealed demand for such solutions, with 72 percent of respondents agreeing that their company needs tools to reduce multi-cloud complexity. When speaking with Lopez Research, potential buyers expressed a desire for a multi-cloud management solution. Ideally, organizations would like to provide an environment where developers can provision services from any cloud provider without understanding each cloud provider’s specific setup and configuration details. However, many IT leaders claimed multi-cloud services were too good to be true.
Simplifying the multi-cloud management challenge
Companies need to simplify cloud management, but the question is how to accomplish such a Herculean task. Recognizing an opportunity, VMware introduced a set of multi-cloud solutions to abstract complexity, similar to how it created virtualization to abstract the underlying hardware layer in the data center.
VMware’s Cross-Cloud services portfolio aims to create a unified way to build, operate, access, and secure any application on any cloud from any device. The portfolio includes services for application development, cloud management, cloud infrastructure, security and networking, and secure user access. At VMware Explore 2022, VMware also introduced a new multi-cloud management portfolio—VMware Aria—which will provide end-to-end solutions for managing cloud-native applications and infrastructure. The portfolio promises to help customers manage their costs, performance, configuration, and delivery across public and private clouds.
For a separate project, I interviewed Vittorio Viarengo, the Vice President of Cross-Cloud Services at VMware, to learn more about the company’s perspective on multi-cloud management. During my discussion with Viarengo, I shared the skepticism I heard from IT leaders. He countered, “We are used to that objection because we heard it with virtualization. There is always a tradeoff between performance versus simplicity when you create abstractions. It comes down to the ability to manage multiple items versus going deep into a specific area.”
The many faces of multi-cloud management
My conversations with large cloud computing providers yield a slightly different perspective. Hyperscalers argue they provide all the management and security tools for their clouds and offer solutions that support multi-cloud environments. Meanwhile, Viarengo says the choice isn’t binary. In some areas, you’ll need to use the specific tools of one cloud; in other cases, you can simplify management by using cross-cloud services. It’s about leveraging the best of both worlds.
He said, “For example, when using VMware Cross-Cloud services, like Aria for management or Tanzu for application development, we never hide the underlying infrastructure. If you’re deploying something on Google or Azure, the company has access to all the underlying APIs. We provide a customer running applications in multiple places, such as on Microsoft’s Azure, on-prem and on AWS, with a single console to manage applications across all clouds. Developers can check the health and performance of applications across locations. Now, if a developer wants to stop a machine learning workload in a specific cloud (e.g., Google Cloud), the developer will have to log in to that cloud to terminate the workload.”
In my mind, Viarengo’s comments surface an important market distinction and opportunity. Often, developers embrace a particular cloud for its specific features, and the buyer would want to preserve those benefits. However, technology leaders also need visibility for application performance and security across clouds. An abstraction layer provides balance for both developers and cloud admins. Viarengo explained that cross-cloud services like Aria address application performance issues and security data risks by providing a company with visibility across all its cloud assets. He said, “Without the visibility and models of Aria Hub and Aria Graph, you cannot answer questions, like how much these applications cost, how are they performing, and what is the infrastructure they’re running on? Have you properly configured the security? Without that model, Operations must collect that information from multiple consoles.”
Monocloud and repatriation are terms we also hear IT leaders discuss. However, IT leaders will not return to the days when most of their applications resided in private data centers or used a single public cloud. Lopez Research expects companies to rightsize cloud computing investments but continue to place workloads in the best location to achieve the task at hand. Sometimes this is a specific public cloud or on-premises. Viarengo said, “If we agree that 75% of organizations are using two or more or more clouds, that is where the value of a cross-cloud service comes in.”
Where do you begin?
While every company is at a different place in its cloud journey, I asked Viarengo to categorize the first several use cases a company should adopt. He shared the following advice. First, a company should anchor its technology deployments to its business goals, such as saving money, increasing agility, or growing revenue.
Viarengo said there are several use cases that many types of companies could employ. One potential use case is the ability to switch deployment environments quickly to meet cost, functionality, or resiliency requirements. Cross-cloud application management, using tools such as ARIA, is another important consideration, as is security. For example, using a service mesh like Tanzu can allow for creating Service Level Agreements (SLAs) and automatically scaling microservices across multiple clouds. Meanwhile, topology visibility and micro-segmentation improve security by ensuring that the same security policies are applied consistently to every cloud in your cloud estate. These are just a few examples of the many possibilities that a multi-cloud management strategy can offer.
Technology never stands still.
The cloud continues to evolve, and so does VMware. Organizations have been working with VMware since the late 1990s; much has changed. At the close of our discussion, I asked Viarengo to share what’s different about VMware that may not be evident today.
He said, “I’d love our customers and prospects to realize this is a new VMware. We’ve been so successful in the private cloud, but over the last five years, we have pivoted the company to address this multi-cloud world. We are using the lingua franca of the native cloud, solving multiple problems with APIs, microservices, and Kubernetes. There are use cases where it makes sense for enterprise workloads to use vSphere and VMware Cloud, but VMware is increasingly investing in solving the problems of native multi-cloud.” The key phrase in this discussion is native cloud. Any vendor that wants to be successful must have a set of solutions designed from the ground up as cloud-native services.
“VMware believes in enterprise pragmatism. We realize that every customer is on a different journey. Some companies have on-premises infrastructure and applications they want to modernize and optimize with vSphere. If they move to the cloud, we can help them move hundreds of workloads in months, not years, with VMware Cloud. If they are cloud-native and building applications across multiple clouds, they have Cross-Cloud services to help them with multi-cloud. We allow companies to adopt the cloud at their own pace. This is what differentiates VMware from the competition and excites me.”
Key Takeaways.
The multi-cloud management problem is real. From where I sit, it’s clear that VMware’s picked up on a market need, but that doesn’t mean it’s without its challenges. Cloud services don’t stand still, so VMware must rapidly innovate to prove it is the cross-cloud provider of choice. Additionally, I expect more software technology vendors to mimic VMware’s approach. Viarengo acknowledges this. Even cloud providers like Google Cloud, with its Anthos product, want to offer a version of Cross-Cloud services. Still, it’s good to see established providers like VMware weigh in to provide support for such a critical use case. CIOs need to focus on creating a multi-cloud strategy rather than firefighting the randomness of supporting multiple cloud services. -
26: 26. Dean Bogdanovic, Alef on vRANs, Private Edge and More
Alef CTO Dean Bogdanovic on vRANs, private edge and modern edge networks
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25. Greg Jones, CTO of Kajeet Discusses Smart IoT Connectivity
Greg Jones has Global experience as a Chief Information Officer, Chief Technology Officer, and Chief Data Officer spanning more than 30 years in the technology. Extensive experience in Mobile Communications, Heathcare, Education, and Management Consulting. He has led startups through Strategic Sale/IPO, as well as managed global teams of nearly 1000 people in major corporations. Expertise in product management, technology innovation, agile development, SaaS, AI/Machine learning, and Cloud Computing.
