Organizations across industries are looking to AI to turn troves of data into intelligence, powered by the latest advances in generative AI. Yet for many organizations, there is a barrier to adopting the latest models because they can be costly to train or serve. A new class of cloud GPUs is needed to lower the cost of entry for businesses that want to tap the power of AI. 

Today, we’re introducing G2, the newest addition to the Compute Engine GPU family in Google Cloud. G2 is the industry’s first cloud VM powered by the newly announced NVIDIA L4 Tensor Core GPU, and is purpose-built for large inference AI workloads like generative AI. G2 delivers cutting-edge performance-per-dollar for AI inference workloads that run on GPUs in the cloud. By switching from NVIDIA A10G GPUs to G2 instances with L4 GPUs, organizations can lower their production infrastructure costs up to 40%. We also found that customers switching from NVIDIA T4 GPUs to L4 GPUs can achieve 2x-4x better performance. As a universal GPU offering, G2 instances also help accelerate other workloads, offering significant performance improvements on HPC, graphics, and video transcoding. Currently in private preview, G2 VMs are both powerful and flexible, and scale easily from one up to eight GPUs. 

Currently organizations require end-to-end enterprise ready infrastructure that will future proof their AI and HPC initiatives for a new era. G2s will be ready to be deployed on Vertex AI, GKE, and GCE, giving customers the freedom to architect their own custom software stack to meet their performance requirements and budget. With optimized Vertex AI support for G2 VMs, AI users can tap the latest generative AI models and technologies. With an easy to use UI and automated workflows, customers can access, tune and serve modern models for video, text, images, and audio without the toil of manual optimizations. The combination of these services with the power of G2 will help customers harness the power of complex machine models for their business.

NVIDIA L4 GPUs with Ada Lovelace Architecture

G2 machine families enable machine learning customers to run their production infrastructure in the cloud for a variety of applications such as language models, image classification, object detection, automated speech recognition, and language translation. Built on the Ada Lovelace architecture with fourth-generation Tensor Cores, the NVIDIA L4 GPU provides up to 30 TFLOPS of performance for FP32, and 242 TFLOPs for FP16. Newly added FP8 support, on top of existing INT8, BFLOAT16 and TF32 capabilities, makes the L4 ideal for ML inference. 

With the latest third-generation RT Cores and DLSS 3.0 technology, G2 instances are also great for graphics-intensive workloads such as rendering and remote workstations when paired with NVIDIA RTX Virtual Workstation. NVIDIA L4 provides 3x video encoding and decoding performance, and adds new AV1 hardware-encoding capabilities. For example, G2 can enable gaming customers running game engines such as Unreal and Unity with modern graphics cards to run real-time applications. Likewise, media and entertainment customers that need GPU-enabled virtual workstations can use the L4 to create photo-realistic, high-resolution 3D content for movies, games, and AR/VR experiences using applications such as Autodesk Maya or 3D Studio Max.

What customers are saying

A handful of early customers have been testing G2 and have seen great results in real-world applications. Here are what some of them have to say about the benefits that G2 with NVIDIA L4 GPUs bring:

AppLovin

AppLovin enables developers and marketers to grow with market leading technologies. Businesses rely on AppLovin to solve their mission-critical functions with a powerful, full stack solution including user acquisition, retention, monetization and measurement.

“AppLovin serves billions of AI powered recommendations per day, so scalability and value are essential to our business,” said Omer Hasan, Vice President, Operations at AppLovin. “With Google Cloud’s G2 we’re seeing that NVIDIA L4 GPUs offer a significant increase in the scalability of our business, giving us the power to grow faster than ever before.”

WOMBO

WOMBO aims to unleash everyone’s creativity through the magic of AI, transforming the way content is created, consumed, and distributed. 

“WOMBO relies upon the latest AI technology for people to create immersive digital artwork from users’ prompts, letting them create high-quality, realistic art in any style with just an idea,” said Ben-Zion Benkhin, Co-Founder and CEO of WOMBO. “Google Cloud’s G2 instances powered by NVIDIA’s L4 GPUs will enable us to offer a better, more efficient image-generation experience for users seeking to create and share unique artwork.”

Descript

Descript’s AI-powered features and intuitive interface fuel YouTube and TikTok channels, top podcasts, and businesses using video for marketing, sales, and internal training and collaboration. Descript aims to make video a staple of every communicator’s toolkit, alongside docs and slides. 

“G2 with L4’s AI Video capabilities allow us to deploy new features augmented by natural-language processing and generative AI to create studio-quality media with excellent performance and energy efficiency” said Kundan Kumar, Head of Artificial Intelligence at Descript.

Workspot

Workspot believes that the software-as-a-service (SaaS) model is the most secure, accessible and cost-effective way to deliver an enterprise desktop and should be central to accelerating the digital transformation of the modern enterprise.

“The Workspot team looks forward to continuing to evolve our partnership with Google Cloud and NVIDIA. Our customers have been seeing incredible performance leveraging NVIDIA’s T4 GPUs. The new G2 instances with L4 GPUS through Workspot’s remote Cloud PC workstations provide 2x and higher frame rates at 1280×711 and higher resolutions” said Jimmy Chang, Chief Product Officer at Workspot.

Pricing and availability

G2 instances are currently in private preview in the following regions: us-central1, asia-southeast1 and europe-west4. Submit your request here to join the private preview, or to receive a notification as when the public preview begins. Support will be coming to Google Kubernetes Engine (GKE), Vertex AI, and other Google Cloud services as well. We’ll share G2 public availability and pricing information later in the year.

There’s never been a better time to talk about multicloud, and the Google Cloud Multicloud Mindset series on Twitter Spaces was created to do just that! This series takes place once every two weeks and features live conversations with top experts about the latest multicloud topics. You can join the 15-minute Q&A to ask your top questions and listen to episodes later offline for up to 30 days after we chat.

If you happened to miss our last few episodes, we recommend checking out our introduction blog to the series for what you missed. Let’s dive into our latest episodes, discussing the impact of open source and novel security challenges in multicloud environments.

Episode #5: ‘The intersection of open source and multicloud’

Open source technology has been an integral part of computing since its earliest era, predating even the birth of technology hubs like Silicon Valley. Open source projects have been responsible for giving us some of the most popular software in the world, such as Mozilla Firefox and the operating system Linux.

In the fifth episode, we sat down with Mike Coleman, Cloud Developer Advocate at Google Cloud, and took a closer look into the history of open source technologies, the role they play in a multicloud world, and the developer perspective on using these technologies to do their work.

The concept of multicloud anchors on the ability to run workloads across clouds and being able to pick the providers that are best suited for specific parts of workloads. Adopting open source technologies and languages empower companies to use the tools they need, regardless of cloud provider, without the fear of getting locked into a specific provider.

“As you think about moving across different environments, whether that be cloud to cloud, or developer desktop to ultimate destination, whether that be your data center or the cloud. Open source software allows you to do that…and multicloud is just an extension of that. This idea that I need to run the same software wherever I go.” — Mike Coleman, Cloud Developer Advocate at Google Cloud

If you’ve ever wanted a developer’s take on the impact of multicloud and the influence of open source in software development and digital transformation trends, you’ll want to tune into this episode.

You can access the full conversation on Twitter Spaces.

Episode #6: ‘Novel challenges in security with multicloud’

In the sixth episode of the series, we chatted with Dr. Anton Chuvakin, Security Advisor at Office of the CISO at Google Cloud, about how security leaders and architects are shifting away from traditional security models, which are increasingly insufficient for multicloud environments.

As more organizations adopt multicloud approaches, the question of how to maintain security in these complex environments and the increasing burden on SecOps teams is top of mind. As Dr. Chuvakin noted, the challenges in the cloud facing more traditional teams range from types of telemetry and logs to volumes and lack of clarity on detection use cases. However, these issues intensify when extended to include multiple clouds, where learning how to do something on one provider may be completely different on another.

“If you end up multicloud, you need to know public cloud and how it works at a better level than you would if you’re going to a single provider. Just like if you’re trying to repair three cars, you need to first learn how to repair cars. You need to have more cloud knowledge to do multicloud, not less. You need to have more powerful superpowers in the public cloud computing area because you can’t just learn one provider and call it a day.” — Dr. Anton Chuvakin, Security Advisor at Office of the CISO at Google Cloud

During the discussion, he offered three tips for tackling multicloud security:

1. Learn cloud more, not less if you’re going multicloud. Multicloud requires more cloud knowledge because you can’t learn a single provider and call it a day. You’ll need to understand the differences in order to be able to secure multiple cloud environments.
2. Focus on learning cloud identity management and how it compares to your traditional identity management service functions. Start with identifying the differences and similarities in what you see in one cloud and then continue with other clouds you use.
3. Explore where your threat areas change in cloud environments when you plan detection and response activities to understand if your detection is covered across clouds.

If your organization is embracing multicloud, this is a great episode to listen and learn more about cloud security, the primary considerations and challenges facing security teams, and some helpful best practices for thinking about security in multicloud environments.

We’ll be sharing the latest topics and episodes with you every month in this blog series. Until next time.