Server manufacturers have long recognized the niche in public cloud computing that physical servers fill perfectly. This has evolved over time, with IT leaders and the industry recognizing that some workloads will always be run on-premises; some can run on both the public cloud and on-premises; and some may be entirely cloud-based.
Artificial intelligence (AI) inference is the workload now gaining traction among server vendors as they seek to address concerns about data loss, data sovereignty, and potential latency issues when analyzing AI data from edge devices and the Internet of Things (IoT).
Dell Technologies has now expanded its Dell NativeEdge operating software platform to simplify how organizations deploy, scale and use AI at the edge.
Dell’s platform offers what the company describes as “large-scale device integration”, remote management and multi-cloud application orchestration. According to Dell, NativeEdge provides high availability capabilities to maintain critical business processes and edge AI workloads, which can continue to run regardless of network disruptions or device failures. The platform also offers virtual machine (VM) migration and automatic failover of applications, compute and storage, which Dell says provides organizations with increased reliability and continuous operations.
One of its customers, Nature Fresh Farms, uses the platform to manage more than 1,000 IoT-enabled facilities. “Dell NativeEdge helps us monitor infrastructure elements in real time, ensuring optimal conditions for our products, and receive comprehensive insights into our product packaging operations,” said Keith Bradley, vice president of information technology at Nature Fresh Farms.
Coinciding with the KubeCon North America 2024 conference, Nutanix announced support for hybrid and multi-cloud AI based on the new Nutanix Enterprise AI (NAI) platform. This can be deployed on any Kubernetes platform, at the edge, in central data centers and on public cloud services.
Nutanix said NAI offers a consistent hybrid multi-cloud operating model for accelerated AI workloads, helping organizations securely deploy, run and scale inference endpoints for large models (LLM) to support the deployment of generative AI (GenAI) applications in minutes, not days. or weeks.
It’s a similar story at HPE. At the company’s AI Day in October, HPE CEO Antony Neri explained how some of its enterprise customers need to deploy small language AI models.
“They typically choose a large, commercially available language model that meets the needs and refine those AI models using their unique and very specific data,” he said. “We see that most of these were on-premises loads and colocations where customers are in control of their data, given their concerns around data sovereignty and regulation, data leaks and cloud API security public of AI.”
In September, HPE unveiled a collaboration with Nvidia resulting in what Neri describes as “a complete turnkey private cloud stack that enables businesses of all sizes to easily develop and deploy generative AI applications.”
He said that in just three clicks and less than 30 seconds of deployment, a customer can deploy an HPE private cloud AI, which integrates Nvidia accelerated computing network and AI software with the AI server, the storage and cloud services from HPE.
At its Tech World event in October, Lenovo unveiled Hybrid AI Advantage with Nvidia, which it says combines full-stack AI capabilities optimized for industrialization and reliability.
The AI portion of the package includes what Lenovo calls “a library of ready-to-customize AI use case solutions that help customers overcome barriers to AI ROI.”
The two companies have partnered closely to integrate Nvidia’s accelerated computing, networking, software and AI models into the modular Lenovo Hybrid AI Advantage.
Edge AI with hyperscalers
THE public cloud platforms all provide feature-rich environments for GenAI, machine learning, and running inference workloads. They also have product offerings to address AI inference on IoT and Edge Computing devices.
Amazon Web Services offers the SageMaker Edge agent; Azure IoT Hub is part of the Microsoft offering mix; and Google has Google Distributed Cloud. Such offerings typically focus on the heavy lifting, namely machine learning, using resources available in their respective public clouds to create data models. These are then deployed to power inference workloads at the edge.
What seems to be happening with traditional server companies is that in response to the threat of AI in the cloud, they are seeing a number of opportunities. IT departments will continue to purchase and deploy on-premises workloads, and AI at the edge is one such area of focus. The second factor likely to influence IT buyers is the availability of plans and models to help them achieve their company’s AI goals.
According to analyst Gartner, while public cloud providers have been very successful in showing the art of the possible with AI and GenAI, they have not been particularly good at helping organizations achieve their AI goals. ‘AI.
Speaking at the recent Gartner Symposium, Daryl Plummer, chief research analyst at Gartner, warned that technology vendors are focusing too much on advancing AI from their perspective, without taking customers into the journey to achieve the goals of these advanced AI systems. “Microsoft, Google, Amazon, Oracle, Meta and OpenAI have made a major mistake: They show us what we can do, (but) they don’t show us what we should do,” he said.
The missing pieces are domain expertise and IT products and services that can be tailored to a client’s unique needs. This certainly looks like the area Dell, HPE and Lenovo will look to expand into in partnership with IT consulting firms.