Artificial intelligence (AI) is front and center for many enterprises because of its potential to deliver significant benefits to both top and bottom lines of businesses. It can help enterprises make greater decisions by leveraging their data to reduce costs by improving process efficiencies, and to increase revenues and gain competitive benefit by bringing new offerings to market more swiftly.
 
Further, AI has massive potential to impact human lives. AI can help deliver better healthcare by reducing human errors in diagnosis, enhancing understanding and treatment of diseases and dramatically accelerating drug discovery. It can also be utilized to enhance personal security by identifying potential threats from humans or nature before they become an issue, such as more accurate weather forecasts, or enhanced, in-car traffic monitoring. 
 
Many of the intelligent applications delivering these transformational benefits in enterprises need huge amounts of data, particularly using new means like deep learning, which enables AI today. Much of this data still lives in on-premise data centers due to security restrictions, privacy laws or even the economics of rolling sheer amounts of data. These issues are much more prominent in certain verticals like healthcare, manufacturing, finance and research organizations. When developing and deploying AI applications in these scenarios, it makes more sense to bring the compute power closer to the data than the other way around.
 
To be effective with AI work, companies need more than just a hardware provider for their computing needs. They need a partner who will work hand-in-hand to help minimize business uncertainty and technical complexity associated with this emerging technology. Lenovo is taking a unique approach to meet customer needs through some offerings, which goes beyond infrastructure. Together with Launch:AI Workshops, Lenovo’s global AI Innovation Centers help customers identify use cases that can deliver business value and execute proof-of-concepts by providing AI expertise and optimized infrastructure to lessen business risk and prove technical viability.
 
The next phase of executing AI projects even bring many more obstacles, primarily in procuring hardware and software tools to assist data scientists and developers while optimizing total cost of ownership (TCO). Currently, much of the AI development using deep learning method makes use of open source software frameworks such as TensorFlow, Caffe, MxNet, etc., whereas enterprise IT experience is mostly with packaged software applications, which are easy to manage. Adding further complexity is the need to maintain multiple builders using various frameworks and versions to accomplish the same task. In this context, enterprises need supporting development tools to leverage open source efficiently. Our award-winning Lenovo intelligent Computing Orchestration (LiCO) simplifies AI development by efficiently managing cluster resources, open source frameworks and typical AI workflows.
 
Procuring infrastructure for AI is a balancing act for many data center IT admins because of the need to satisfy numerous requirements. These typically include meeting the several performance demands from data scientists, AI engineers and software engineers, while optimizing the TCO. A hard needle to thread!  The optimal solution would have the performance of a purpose-built system, yet have the flexibility to run multiple applications, even non-AI workloads. With this blueprint in mind, the Lenovo ThinkSystem SR670 was designed basically for scale-out AI workloads, but with flexibility to handle traditional high performance computing (HPC), virtual desktop infrastructure (VDI), video processing, etc. Further, it efficiently scales from experimentation with a couple of nodes to large-scale deployments in distributed training environments, which LiCO also supports as an added benefit.
 
The ThinkSystem SR670 has two models, each based on the different AI workloads in data center, supporting either NVIDIA T4 or NVIDIA V100 Tensor Core GPUs. The most recent ThinkSystem SR670 offering (announcing at NVIDIA GTC’19) supports eight T4 GPUs in a 2U server. With NVIDIA NGC-Ready validation, T4 servers can exceed across the whole range of accelerated workloads— machine learning, deep learning, virtual desktops, and high-performance computing (HPC). And, at 70 watts each, the T4 GPUs alleviate the power and cooling requirements to populate these servers at scale in existing enterprise data centers.
 
For deep learning training or running HPC applications exclusively, the SR670 currently supports four NVIDIA V100 GPUs, and delivers the best total compute capacity with 448 deep learning teraflops or 28 double-precision teraflops. Further, the 32GB on-board HBM2 memory is suitable for training large neural networks and HPC computational models with in-memory requirements. Together, with the dedicated PCIe communication channels between CPU and GPUs in ThinkSystem SR670, this configuration becomes the ideal choice for IT to get the best performance for these two workloads.
 
By using the SR670 and LiCO, IT admins will be able to balance their performance requirements and TCO by designing their data center infrastructure for a better return on investment (ROI).  Now is the time to bring AI closer to the ‘data’ center for building production-grade intelligent applications.
 
Interested in learning more about Lenovo’s AI offerings and the ThinkSystem SR670? Visit the Lenovo booth at GTC ’19 in San Jose from March 18th-21st, to chat with our team about beginning your AI journey.




This article is originally posted on Tronserve.com