Deep neural network and AI in machine learning can be categorized as parallel process which means parallel super computing solutions can speed up 90%. Artificial Intelligence AI and machine learning play a vital role in continuing competitive advantage and delivering fantastic user experience. GPU accelerates the tensor processing necessary for deep learning applications; these deep learning approaches have shown impressive performances in resembling humans in various fields.

GPUs have attracted a lot of attention as the optimal vehicle to run AI workloads. Most of the cutting-edge research seems to rely on the ability of GPUs and newer AI chips to run many deep learning workloads in parallel. However, GPUs are not necessarily better than CPUs when it comes to AI. The trusty CPU still has an important role in AI. CPUs have long held the advantage for certain kinds of AI algorithms involving logic or intensive memory requirements. How to run more efficient AI algorithms on CPUs and GPUs which having a more efficient algorithm and reduces power requirements, makes more practical for applications will drastically expand the market for the application of AI.

In order to having more powerful handling of database workloads, 7Starlake offers an extensive range of CPUGPU servers with multiple GPUCPU combinations dedicated servers like 1CPU+1GPU, 2CPU+4 GPU, custom-built for massive parallel computing environments and today’s most demanding HPC and hyperscale data center workloads  for any high performance demanding tasks.

Each computer is named after several factors—along with solution, number of CPU slots and GPU slots, brand of Intel processor, and series of NVIDIA card system configurations—to be considered when deciding the right computer for clients’ needs. The above illustration demonstrates the naming rule and the pattern of a product name.

Intel® Xeon® Processor D-1500 Product Family Boadwell DE: