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TESLA series GPU for data center servers

? ? ? ? Use NVIDIA^??Tesla^??GPU Faster processing of the most demanding high-performance computing and very large data center workloads. Now, data scientists and researchers can analyze PB-level data in applications such as energy exploration and deep learning, which is several orders of magnitude faster than using traditional CPU. In addition, the Tesla accelerator provides the capabilities needed to run large simulations faster than ever before. Tesla Accelerator is an ideal choice for accelerating the running speed of virtual desktop for any user anywhere.

Using NVIDIA data center platform
Faster processing of data center workloads

Train

Reason

To improve the work efficiency of data scientists and provide artificial intelligence services faster, the key is to train increasingly complex models faster. The server equipped with NVIDIA Tesla P100 uses the powerful NVIDIA Pascal architecture to shorten the deep learning training time from several months to several hours.

Reasoning is an area where trained neural networks really play a role. With the introduction of new data services such as image, voice, vision and video search, reasoning, as the core of many artificial intelligence services, plays a role in providing answers and suggestions. Compared with a server that only uses a single-socket CPU, a server equipped with a Tesla GPU can provide 27 times the reasoning throughput, so it can save a lot of money.

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High performance computing

High-performance computing data center needs to meet the increasing computing needs of researchers, while not exceeding the tight budget. In the past, deploying a large number of general-purpose computing nodes actually increased the cost greatly, and did not improve the performance of the data center accordingly.

NVIDIA Tesla P100 with PCIe interface enables high-performance computing data centers that handle mixed workloads to replace up to half a rack of general-purpose CPU nodes with an acceleration node, and can provide the same throughput in various high-performance computing applications. By speeding up more than 550 kinds of high-performance computing applications (including all the top 10 high-performance computing applications), all high-performance computing customers can now get greatly improved throughput, easily handle workloads and save costs.

Choose the right NVIDIA Tesla solution for you

Job responsibility	?Popular applications	?Applications mainly used in GPU.	Mixed workload HPC		Super-large scale HPC
user	AI/Deep learning	Hyperscale & ＆HPC data center, which can run applications that extend to multiple GPUs.	Supercomputing, universities, governments, research institutes	?Oil and gas	?Artificial intelligence/deep learning
Optimize for	GPU?Accelerated operation	Maximum absolute performance	?Speed up verification time	?Imaging accuracy	?Training time	?Employment/hobbies/research
operating characteristic	Volta Architecture, Tensor Core, Next Gen NVLink	Hyperscale & ＆HPC data center, which can run applications that extend to multiple GPUs.	?Mixed work requirements	Specific applications, such as RTM	Deep learning frameworks, such as Caffe and TensorFlow.	Deep learning frameworks, such as Caffe and TensorFlow.
Key requirements	·Performance (double precision and single precision) ·Memory size and bandwidth ·Interconnection bandwidth ·can be programmed	·Performance (double precision and single precision) ·Memory size and bandwidth ·Interconnection bandwidth ·can be programmed	·Performance (double precision and single precision) ·Memory size and bandwidth ·Interconnection bandwidth		·Performance (single precision) ·Memory size per GPU ·Interconnection bandwidth	·power consumption ·product mix
Recommend solutions	V100	P100	Mixed workload K80		?Train P40	Reason P4 ? ?