Cloud Workloads, AI, ML Driving NVIDIA's Turing T4 Cloud GPU Adoption

Cloud Workloads, AI, ML Driving NVIDIA's Turing T4 Cloud GPU Adoption

NVIDIA's T4 Cloud GPU is receiving a high rate of adoption among industry players such as Google Cloud Platform, Dell EMC, Hewlett Packard Enterprise, IBM, Lenovo, and Supermicro.

In addition, Baidu, Tencent, JD.com and iFLYTEK have started using NVIDIA's T4 Cloud GPU to expand and accelerate their hyperscale datacenters.

Inspur Power System, Lenovo, Huawei, Sugon, IPS and H3C also announced a wide range of new T4 servers and expected to begin shipping before the end of the year 2018,
  • Inspur NF5280M4 /NF5280M5/NF5288M5/NF5468M5
  • Huawei G2500/2288 HV5/ 5288V5/G530 V5/G560 V5
  • Lenovo ThinkSystem SR630/SR650
  • Sugon X580-G30/X745-G30/X780-G30/X780-G35/X785-G30 / X740-H30
  • Inspur Power System: FP5295G2
  • H3C Uniserver G4900G3
Public and private cloud environments demand horizontal as well as vertical performance due to ever-increasing cloud adoption followed by data growth. NVIDIA T4 GPUC helps maximize throughput, utilization and user concurrency in the cloud environment.

The T4 GPU’s multi-precision Turing Tensor Cores and new RT Cores capabilities along with combined with accelerated containerized software stacks deliver breakthrough AI performance. For a wide range of AI workloads at four different levels of precisions 8.1 TFLOPS at FP32, 65 TFLOPS at FP16 as well as 130 TOPS of INT8 and 260 TOPS of INT4 are possible. With so much of processing power, there is a huge scope of server consolidation. For example, according to the announcement, It is possible for AI inference workloads, a server with two T4 GPUs can replace up to 54 CPU-only servers. For AI training, a server with two T4 GPUs can replace nine dual-socket, CPU-only servers.

The NVIDIA T4 Cloud GPU is used to accelerate AI inference and training in the global high-performance computing market for enterprise and hyperscale. PC: NVIDIA

Roughly the size of a candy bar, the low-profile, 70-watt T4 GPU has the flexibility to fit into a standard server or any Open Compute Project hyperscale server design. Server designs can range from a single T4 GPU all the way up to 20 GPUs in a single node.

T4’s multi-precision capabilities power breakthrough AI performance for a wide range of AI workloads at four different levels of precision, offering 8.1 TFLOPS at FP32, 65 TFLOPS at FP16 as well as 130 TOPS of INT8 and 260 TOPS of INT4. For AI inference workloads, a server with two T4 GPUs can replace 54 CPU-only servers. For AI training, a server with two T4 GPUs can replace nine dual-socket CPU-only servers.

Executive Opinion

Vice president of Accelerated Computing at NVIDIA, Ian Buck, said,  “We have never before seen such rapid adoption of a datacenter processor. Just 60 days after the T4’s launch, it’s now available in the cloud and is supported by a worldwide network of server makers. The T4 gives today’s public and private clouds the performance and efficiency needed for compute-intensive workloads at scale", he further added, “The continued rapid adoption of T4 makes complete sense, given its unprecedented capabilities. Never before have we introduced a GPU that gives public and private clouds the combined performance and energy efficiency they need to more economically run their compute-intensive workloads at scale. And in markets where ‘scale’ really counts, we expect T4 to be extremely popular.”

GCP Early Access

Google Cloud Platform (GCP) is the first major cloud vendor to offer availability for the NVIDIA Tesla T4 GPU in alpha and users can sign up for early access to T4 GPU on GCP here.


PC:pablo,pixabay,nvidia

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Mandar is a seasoned software professional for more than a decade. He is Cloud, AI, IoT, Blockchain and Fintech enthusiast. He writes to benefit others from his experiences. His overall goal is to help people learn about the Cloud, AI, IoT, Blockchain and Fintech and the effects they will have economically and socially in the future.

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