The Tesla T4 GPU server from Nvidia is an impressive GPU designed for various workloads. It can handle cloud-based high-performance workloads like artificial intelligence, machine learning, data analytics, graphics, and deep learning.
The T4 GPU can be deployed in datacenter workstations, clusters, and supercomputers. It works great for scientific and engineering purposes.
The T4 GPU is the latest in the Nvidia line of Tesla GPUs, designed for high-performance computing.
CONDENSED SPECS SHEET
Architecture: Nvidia Turing
Form Factor: Low-profilePCIe
GPU Memory: 16 GDDR6 300GB/s
Energy Consumption: 70-watt
Nvidia Turing Tensor Cores: 320
Nvidia CUDA Cores: 2560
Compute APIs: CUDA, ONNX, Nvidia TensorRT
Single Precision: 8.1 TFLOPS
Mixed Precision: 65 TFLOPS
Powered by Nvidia Turing Tensor Cores, the Tesla T4 is perfect for accelerating AI inference workloads.
With the Tesla, you get up to 40 times advantage over CPU-only servers for NLP (natural language processing), streaming videos, and recommender models.
Its TensorRT cores have runtime engines, an inference server, and an optimizer to deploy applications in production.
The Tesla T4 has powerful RT cores that combine with NVIDIA RTX technology for real-time raytracing for accurate shadows, reflections, refractions, and photorealistic environments and objects.
The 70W power consumption of the Tesla T4 makes it perfectly optimized for scale-out servers, with up to 50 times increase in energy efficiency compared to CPUs. This will save energy and operational costs.
This makes the Tesla T4 GPU the most energy-efficient solution for artificial intelligence inference and training.
The Tesla T4 GPU doesn’t require any additional power connectors. It connects to systems using PCIe 3.0 x16 interface. It has a single-slot cooling system. NVIDIA supports x8 and x16 PCI Express for the T4. The GPU has 13.6 billion transistors.
Tesla T4 GPU Applications
You can use the Nvidia TensorRT for AI model in datacenters. It also integrates perfectly with Docker and Kubernetes to maximize GPU utilization and to support popular AI frameworks.
If you are an app developer, you don’t need to write inference capabilities from the beginning with the Nvidia Tesla T4 GPU.
For DevOps Engineers, the Tesla T4 GPU provides autoscaling, orchestration, and load balancing to deploy inference services for as many applications as possible.
Tesla T4 GPU supports AI, neural network and machine learning libraries/frameworks like PyTorch, TensorFlow, TensorRT, Apache MXNet, Keras, Caffe, Chainer, Microsoft Cognitive Toolkit, model repository, ONNX, and more.
The Tesla T4 GPU has multi-precision AI computing power with a breakthrough performance from FP32 to FP16 to INT8 and INT4 precision. It delivers up to almost ten times more power than CPUs on training and about 36x on interference.
The Tesla T4 comes with an enhanced NVIDIA NVENC encoder for better compression and higher image quality with both H.265 and H.264 video codecs. The Nvidia NVENC encoder that comes with the Tesla T4 allows for up to 25% bitrate savings for the H.265 codec and 15% bitrate savings for the H.264 codec.
The Tesla T4 is powerful for video transcoding. It can efficiently search for and retrieve insights from video. It is suitable for AI video applications, dedicated transcoding engines for high-performance decoding at twice the speed of prior GPUs.
With the Tesla T4, you can decode up to 38 1080p video streams, so deep learning can easily be integrated into video pipelines for smart, innovative video.
Nvidia Tesla T4 GPU Setup
The Nvidia Tesla T4 GPU fits easily into most datacenter infrastructures. You can configure up to six Tesla T4 in one PowerEdge server.
With the Nvidia Tesla T4, you can improve your data center to accommodate HPC and AI applications.
Nvidia Tesla T4 GPU Buying Options
You can get the Nvidia Tesla T4 GPU from various providers. They can customize the Tesla T4 GPU to the specifications of your datacenter. They’ll also provide a multi-year warranty, excellent customer support, and service.
You could get the Nvidia Tesla T4 GPU with various form factors from 1U to 4U, with up to 10 GPUs, 24 drive bays, and two processors.
You could also ask the provider for specific component configurations from processors to hard drives to solid-state drives to network cards to operating systems to controller cards to PCIe storage cards, software, and warranty.
You can evaluate pricing from various competitors to get the most value for your tech investment on the Nvidia Tesla T4 enterprise GPU.
Nvidia Tesla T4 GPU Cloud Providers
Google and some other cloud providers have used the Nvidia Tesla T4 GPU. In 2019, Google announced that it would add the Nvidia Tesla T4 GPUs to its Google Cloud Platform. Google installed the GPU for inference workloads and machine learning training workloads.
Google will also support virtual workloads on the Nvidia Tesla T4 GPUs so that designers can render applications from anywhere.
In 2019, Amazon adopted the Nvidia Tesla T4 GPU for AI interference and machine learning on its Amazon Web Services Cloud platform.
Amazon also uses the Nvidia Tesla T4 GPU for its Luna game streaming service. They paired the GPU with an Intel Cascade Lake GPU. The Amazon Luna game streaming service will compete with Google Stadia, Sony’s PlayStation Now, and Microsoft’s Project xCloud.
The Tesla T4 GPU has also been used in data centers owned by HP, Dell, and Cisco.
Cons Of Nvidia Tesla T4 GPU
The major con of the Tesla T4 GPUs is the lack of monitor support. The GPU was not designed to have any monitor connected to it.
The Tesla T4 is an efficient GPU that consumes minimal power (70W). It delivers high fidelity graphics, machine learning, and AI computing power with high efficiency and low power consumption. It is an excellent GPU for your data center, server, and high-performance computing needs.