HPE announces AI-native architecture and hybrid cloud solutions

Hewlett Packard Enterprise has announced at HPE Discover Barcelona 2023 the next series of AI-native and hybrid cloud offerings for machine learning development, data analytics, AI-optimised file storage, AI tuning and inferencing and professional services. These solutions bring together HPE’s leadership in hybrid cloud, supercomputing and AI/machine learning (ML) software. Everything is based on an open, full-stack AI-native architecture that incorporates a curated mix of software and infrastructure designed specifically to accelerate the AI lifecycle.

“With the emergence of Gen AI, enterprises are quickly realising that the data and computational demands to effectively run AI models require a fundamentally different approach to technology,” said Antonio Neri, President and CEO, HPE. Gen AI stands for generative AI.

“HPE will bring its market-leading hybrid cloud, supercomputing and AI capabilities more broadly to the enterprise to enable an AI-powered transformation, where customers can develop AI models securely with their proprietary data. Through HPE’s AI-native and hybrid cloud solutions, organisations will be able to fully capitalise on the insights from their data to revolutionise product innovation, customer engagement, and overall realise the full power of Gen AI to transform their businesses and industries.”

According to HP, Gen AI workloads are computationally intensive and require the ability to efficiently process massive amounts of data. For enterprises to effectively incorporate Gen AI, deep learning, computer vision or classical machine learning models into their business, they will need to extend their cloud-native environment to include an AI-native approach. The HPE GreenLake cloud platform is strategically positioned to deliver such an evolution in IT through an open, full-stack AI-native architecture featuring:

- Data-first pipeline to manage public and proprietary data across multi-generational IT

- AI lifecycle management software to accelerate workflows for training, tuning and inferencing

- Hybrid by design to run AI anywhere from edge to cloud with data protection

- High-performance interconnects for intelligent connectivity and traffic management for large clusters

- Supercomputing DNA built into the entire portfolio, sustainable by design, to train the largest models - Open ecosystem for freedom of choice with no lock-in

The new AI-native offerings include AI-native solution stacks with NVIDIA, as well as AI-native infrastructure, software and services for AI model development and deployment.

An expanded HPE collaboration with NVIDIA aims to deliver an enterprise-class, full-stack Gen AI solution. The co-engineered, preconfigured AI tuning and inferencing solution enables enterprises of any size to quickly customise foundation models using private data and deploy production applications anywhere, from edge to cloud. The offering removes the complexity of developing and deploying Gen AI infrastructure.

“Generative AI is inspiring enterprises to reinvent their businesses using their own data as the rocket fuel for transformation,” said Manuvir Das, VP, enterprise computing, NVIDIA.

“Our extended collaboration with HPE will help enterprises everywhere harness full-stack, accelerated computing and software from NVIDIA to supercharge the power of generative AI.”

HPE GreenLake for File Storage, an all-flash unstructured data platform with a cloud operational experience, keeps pace with customers’ large-scale AI workloads. New enhancements will speed AI model training and tuning, including Gen AI and large language models (LLMs), plus accelerate data aggregation and data preparation. Support for 30 TB NVMe SSDs and connectivity to new NVIDIA Quantum-2 InfiniBand for GPU-centric compute are available today. Upcoming enhancements will increase the capacity density and throughput by seven times*, and will be available early first half of 2024.

HPE Machine Learning Development Environment Software is now available as a managed service on AWS and other cloud providers.

- Accelerate and securely implement Gen AI initiatives in days with a flexible managed service.

- Reduce the complexity and operational overhead with an AI/ML model training managed service to accelerate the time-to-model development.

- AI/ML model training infrastructure to help relieve management staffing and processing burdens.

- Increase AI adoption with new generative AI studio capabilities to rapidly prototype and test models.

HPE Ezmeral Software provides a hybrid software-as-a-service (SaaS) foundation for data, analytics and AI. New enhancements further simplify and accelerate enterprise data, analytics and AI with an end-to-end platform that seamlessly operates across hybrid multicloud environments.

- Speed up time to insight across the entire analytics lifecycle with the power of a hybrid data lakehouse that is now GPU- and CPU-optimised and can manage, access and analyse data across any NFS- or S3-compliant solution.

- Enhanced model training and tuning in HPE Ezmeral Unified Analytics Software via deep integration with HPE Machine Learning Development Environment Software

- Optimise NVIDIA GPU allocations across workloads and users with GPU-aware capabilities in HPE Ezmeral Unified Analytics Software

- Access expanded third-party integrations with Whylogs for model observability and Voltron Data for GPU-accelerated queries

The new enterprise computing solution for Gen AI is also available as an HPE GreenLake Flex Solution that includes HPE GreenLake for File Storage with Zerto Cyber Resilience Vault software to protect AI models and data sources and OpsRamp software to provide visibility and automation across the AI lifecycle in multivendor, multicloud environments.

HPE Services now provides a portfolio of consulting services, workforce training and deployment solutions. The new AI services take customers from Gen AI and LLM discovery to implementation, where customers develop the optimum operational models and hybrid cloud data strategies needed to build, deploy and scale solutions into transformative outcomes. These services are supported by new Global Centers of Excellence for AI and Data, including in India.

In mid-November, HPE announced a supercomputing solution for generative AI designed for large enterprises, research institutions, and government organisations that offers unprecedented scale and performance for big AI workloads, such as large language model (LLM) and deep learning recommendation model (DLRM) training.

The solution comprises a software suite enabling customers to train and tune models and develop AI applications. The solution also includes liquid-cooled supercomputers, accelerated compute, networking, storage, and services.

Key components of the solution include software to build AI applications, customise prebuilt models, and develop and modify code. The software is integrated with HPE Cray supercomputing technology, and powered by NVIDIA Grace Hopper GH200 Superchips.

The advanced supercomputing capabilities of HPE, supported by NVIDIA technology, improve system performance by by 2-3x*, HPE said. Using HPE Machine Learning Development Environment on this system, the open source 70 billion-parameter Llama 2 model was fine-tuned in under 3 minutes**.

Details

The enterprise computing solution for generative AI can be ordered from Q124.

*HPE GreenLake for File Storage

**Standard AI benchmarks BERT and Mask R-CNN, using an out-of-box, non-tuned system comprising the HPE Cray EX2500 Supercomputer using an HPE Cray EX254n accelerator blade with four NVIDIA GH200 Grace Hopper Superchips. The independently-run tests showed 2-3X performance improvement as compared to MLPerf 3.0 published results for an A100-based system comprising two AMD EPYC 7763 processors and four NVIDIA A100 GPUs with NVLINK interconnects.

***Using 32 HPE Cray EX 2500 nodes with 128 NVIDIA H100 GPUs at 97% scaling efficiency, a 70 billion-parameter Llama 2 model was fine-tuned in internal tests on a 10 million token corpus in less than 3 minutes. Model tuning code and training parameters were not optimised between scaling runs.

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