Google Cloud grabs serving spoon, ladles instances loaded with Nvidia’s Tesla T4 GPUs

For the diner that wants to chow down on ML and analytics data but can't stomach Tesla Volta V100 costs


Google has become the first bringer of clouds to sell server instances equipped with Nvidia’s Tesla T4 GPUs in general availability.

The new instances are aimed at organisations that work with machine learning, analytics and rendering, but don’t require the power of Nvidia’s beefier and considerably more expensive Tesla Volta V100.

Nvidia’s T4 GPUs feature 320 Turing Tensor cores for faster AI training, 2,560 CUDA cores, and 16 GB of GDDR6 memory, as well as dedicated RT cores for the all-important real-time ray tracing, a rendering technique that simulates properties of light for realistic-looking 3D images.

The card supports automatic mixed precision functionality across FP32, FP16, INT8 and INT4, originally announced at Nvidia’s GPU Technology Conference (GTC) 2019.

The silicon, Google said, can bench up to 130 TFLOPS when used for machine learning inference – the process of analysing information and generating output based on ML training parameters.

A single T4 currently retails for around £2,600 ($3,386) in the UK, but the cloud versions are available from $0.29 (£0.22) per hour per GPU on ‘preemptible virtual machines’ – Google’s term for its cheapest VMs that self-destruct after 24 hours and can be terminated earlier, if the Chocolate Factory suddenly needs their resources.

For serious work that doesn’t tolerate disruptions, Google Cloud now sells custom on-demand instances that can be equipped with up to four of Nvidia’s absolute units, 96 vCPUs, 624GB of RAM and up to 3 TB of local SSD storage.

Along with the new instances, Google has deep learning VM images, preconfigured with the software tools necessary to start training your own personal robot overlords, including drivers, CUDA-X AI libraries, TensorFlow and PyTorch.

“We handle software updates, compatibility, and performance optimizations, so you don’t have to. Just create a new Compute Engine instance, select your image, click Start, and a few minutes later, you can access your T4-enabled instance,” said Chris Kleban, product manager for Cloud GPUs at Google Cloud Platform.

Google Cloud launched an alpha of the service in November 2018, and a beta in January across eight geographic markets (but not the UK).

Snap Inc, the parent of Snapchat, has been using T4-based instances for a while. Surprisingly, its application has nothing to do with the images exchanged on the platform: “Snap’s monetization algorithms have the single biggest impact to our advertisers and shareholders,” claimed Nima Khajehnouri, senior director for monetization at Snap.

GCP might be the first public cloud to launch Tesla T4s as a global service, but it will not enjoy this advantage for long: last month at GTC, AWS said its G4 instances with Nvidia’s latest silicon on board would be available within weeks. These VMs will offer up to eight T4s, up to 384GB of RAM, and up to 1.8TB of local NVMe storage. ®

Similar topics

Broader topics


Other stories you might like

  • DuckDuckGo tries to explain why its browsers won't block some Microsoft web trackers
    Meanwhile, Tails 5.0 users told to stop what they're doing over Firefox flaw

    DuckDuckGo promises privacy to users of its Android, iOS browsers, and macOS browsers – yet it allows certain data to flow from third-party websites to Microsoft-owned services.

    Security researcher Zach Edwards recently conducted an audit of DuckDuckGo's mobile browsers and found that, contrary to expectations, they do not block Meta's Workplace domain, for example, from sending information to Microsoft's Bing and LinkedIn domains.

    Specifically, DuckDuckGo's software didn't stop Microsoft's trackers on the Workplace page from blabbing information about the user to Bing and LinkedIn for tailored advertising purposes. Other trackers, such as Google's, are blocked.

    Continue reading
  • Despite 'key' partnership with AWS, Meta taps up Microsoft Azure for AI work
    Someone got Zuck'd

    Meta’s AI business unit set up shop in Microsoft Azure this week and announced a strategic partnership it says will advance PyTorch development on the public cloud.

    The deal [PDF] will see Mark Zuckerberg’s umbrella company deploy machine-learning workloads on thousands of Nvidia GPUs running in Azure. While a win for Microsoft, the partnership calls in to question just how strong Meta’s commitment to Amazon Web Services (AWS) really is.

    Back in those long-gone days of December, Meta named AWS as its “key long-term strategic cloud provider." As part of that, Meta promised that if it bought any companies that used AWS, it would continue to support their use of Amazon's cloud, rather than force them off into its own private datacenters. The pact also included a vow to expand Meta’s consumption of Amazon’s cloud-based compute, storage, database, and security services.

    Continue reading
  • Atos pushes out HPC cloud services based on Nimbix tech
    Moore's Law got you down? Throw everything at the problem! Quantum, AI, cloud...

    IT services biz Atos has introduced a suite of cloud-based high-performance computing (HPC) services, based around technology gained from its purchase of cloud provider Nimbix last year.

    The Nimbix Supercomputing Suite is described by Atos as a set of flexible and secure HPC solutions available as a service. It includes access to HPC, AI, and quantum computing resources, according to the services company.

    In addition to the existing Nimbix HPC products, the updated portfolio includes a new federated supercomputing-as-a-service platform and a dedicated bare-metal service based on Atos BullSequana supercomputer hardware.

    Continue reading

Biting the hand that feeds IT © 1998–2022