Unlocking AI and Machine Learning Potential: NVIDIA T4 GPUs on Google Cloud

Summary: NVIDIA T4 GPUs are now available on Google Cloud, offering a powerful tool for machine learning training and inference, high-performance computing, data analytics, and graphics applications. This article explores the capabilities and benefits of using NVIDIA T4 GPUs on Google Cloud, including their technical specifications, pricing, and real-world applications.

Introduction to NVIDIA T4 GPUs

NVIDIA T4 GPUs are built on the Turing architecture, which brings the second generation of Tensor Cores to the table. These GPUs are designed to accelerate diverse cloud workloads, including high-performance computing, deep learning training and inference, machine learning, data analytics, and graphics. Each T4 GPU is equipped with 16GB of GDDR6 memory, delivering 260 TOPS of computing performance.

Technical Specifications of NVIDIA T4 GPUs

  • Memory and Performance:

    • 16GB of GDDR6 memory
    • 256-bit memory interface
    • 1250MHz memory clock speed
    • 320GB/s memory bandwidth
    • 2560 CUDA cores
    • 320 Tensor cores
    • 160 texture mapping units
    • 64 ROPs
    • 40 ray tracing acceleration cores

  • Power and Connectivity:

    • 70W power consumption
    • No additional power connector required
    • PCIe 3.0 x16 interface
    • Single-slot cooling solution
    • No display connectivity

Benefits of NVIDIA T4 GPUs on Google Cloud

Machine Learning and Inference

NVIDIA T4 GPUs are ideal for machine learning training and inference. They offer high-performance characteristics for FP16, INT8, and INT4, allowing for flexible accuracy/performance tradeoffs. For example, when Tensor Cores are enabled with mixed precision, T4 GPUs on Google Cloud Platform (GCP) can accelerate inference on ResNet-50 over 10X faster with TensorRT compared to running only in FP32.

High-Performance Computing and Data Analytics

The T4 GPUs are also great for high-performance computing and data analytics. They support a range of precision, including FP32, FP16, INT8, and INT4, making them versatile for various applications.

Graphics and Virtual Workstations

NVIDIA T4 GPUs are excellent for graphics applications and virtual workstations. With the NVIDIA Quadro Virtual Workstations system in GCP, developers can run their applications on the NVIDIA RTX platform, enabling real-time ray tracing, video and image processing, and AI-enhanced graphics.

Real-World Applications

Snap and Princeton University

Companies and organizations like Snap and Princeton University are leveraging NVIDIA T4 GPUs for inference to accelerate their services and research. For instance, Snap uses T4 GPUs for their monetization algorithms, which have a significant impact on advertisers and shareholders. Princeton University uses thousands of T4 GPUs powered by Kubernetes Engine to reconstruct the connectome of a cubic millimeter of neocortex, tracing 5 km of neuronal wiring and identifying a billion synapses.

Autodesk

Autodesk also benefits from NVIDIA T4 GPUs, particularly for virtual workstations. The NVIDIA Quadro Virtual Workstation on GCP allows Autodesk customers to deploy and start using software quickly from anywhere, seeing significant improvements in rendering scenes and creating realistic 3D models and simulations.

Pricing and Availability

NVIDIA T4 instances are priced at $0.29 per hour per GPU on preemptible VM instances, with on-demand instances starting at $0.95 per hour per GPU. Sustained use discounts can offer up to a 30% discount.

Conclusion

NVIDIA T4 GPUs on Google Cloud offer a powerful and versatile tool for a wide range of applications, from machine learning and high-performance computing to graphics and virtual workstations. With their robust technical specifications and competitive pricing, they are an excellent choice for businesses and researchers looking to accelerate their workloads. Whether it’s accelerating inference, enhancing graphics, or powering virtual workstations, NVIDIA T4 GPUs on Google Cloud are a significant step forward in cloud computing capabilities.