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Mark Lohmeyer shared thisGartner predicts that by 2028, 95% of new AI deployments will run on Kubernetes—up from under 30% in 2025. Container infrastructure is no longer just about microservices; it is the distributed runtime for the agentic era. In the 2026 Gartner® Magic Quadrant™ for Container Management, Google has been named a Leader for the fourth consecutive year, positioned highest in Ability to Execute. In the accompanying Critical Capabilities report, Google Cloud also ranked first across all six evaluated use cases, including AI Training, AI Inference, and Cloud-Native Applications. We introduced Kubernetes in 2014 and GKE in 2015. Today, we're designing container infrastructure from silicon up for autonomous systems—from GKE Agent Substrate delivering 10x runtime density to Cloud Run scaling Blackwell GPUs from zero in under five seconds. Huge credit to our engineering and product teams and the customers building the future with us every day. Read the full analysis and download the report: https://lnkd.in/gRJU5U8U #GoogleCloud #Kubernetes #AIInfrastructure2026 Gartner Magic Quadrant for Container Management | Google Cloud Blog2026 Gartner Magic Quadrant for Container Management | Google Cloud Blog
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Mark Lohmeyer shared thisAccess to raw compute is table stakes for AI startups today. What actually differentiates is architectural choice and the software stack built around it. As Darren highlights in his blog, startups building frontier AI do not want to be locked into single-vendor architectures. Google Cloud gives them the unique ability to choose—and co-deploy—both TPU and GPU clusters tailored to their specific training and inference requirements. Crucially, silicon is never the whole story. Startups scale when that compute is deeply integrated with the full stack: Gemini Enterprise, GKE, BigQuery, and high-throughput storage. Having unified access to that entire ecosystem is what turns raw chips into production systems. Learn more about the three things defining the startup AI stack: https://lnkd.in/gSaWpPi9 #GoogleCloud #AIInfrastructure #StartupsThe three things today's hottest startups are looking for in their AI stack | Google Cloud BlogThe three things today's hottest startups are looking for in their AI stack | Google Cloud Blog
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Mark Lohmeyer shared thisExciting news — Google Cloud was named a Leader in The Forrester Wave™: Public Cloud Platforms (Q3 2026), earning the highest score in the Current Offering category! Even better, we received the highest combined score of any vendor and the top score possible in 23 of the 30 criteria evaluated — including Vision, Innovation, AI Development, GKE / Kubernetes, Databases, and Security. As teams shift from chatbots to autonomous agents, the big takeaway is simple: you can’t run next-gen AI on fragmented infrastructure. You need a platform co-designed from the ground up — from silicon and systems to models and orchestration. What I believe is driving our leadership position: 1. Full-stack co-design: Decades of co-designing our silicon, systems, and software stacks allow customers to scale AI workloads with real enterprise efficiency. 2. Ready for the agentic era: We're continuing to evolve GKE so organizations can run autonomous agents and core workloads on a highly scalable, proven platform. 3. Grounded in live data: Our Agentic Data Cloud bridges operational databases and analytics, turning enterprise data into a real-time reasoning engine. A massive thank you to our incredible engineering, product, and infrastructure teams for their relentless innovation — and to our customers and partners who push us forward every day. Check out the full report: https://lnkd.in/g4MTV3Tv #GoogleCloud #AIInfrastructure #GKE #Kubernetes #GenerativeAI #CloudComputing
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Mark Lohmeyer reposted thisMark Lohmeyer reposted this90% of enterprises plan to deploy AI agents over the next few years, Only 17% think their current infrastructure can actually handle the load. That stat from Mark Lohmeyer in his recent chat with Daniel Newman really captures the shift we're seeing right now. In the chat era, one prompt equaled one response. With agents, a single prompt can trigger hundreds of parallel machine-to-machine sub-agent interactions—driving a 50x to 100x surge in inference requests. And because agents have to call real-world APIs and query existing enterprise databases, they aren't just hammering GPUs and TPUs. They’re putting massive pressure on traditional CPUs, memory, and networking. As Daniel Newman put it: "𝘵𝘩𝘦 𝘳𝘦𝘵𝘶𝘳𝘯 𝘰𝘧 𝘵𝘩𝘦 𝘊𝘗𝘜". If you're looking at how to design infrastructure that can dynamically adapt - scaling across CPUs, GPUs and TPUs without locking your team into rigid hardware silos - this is a great 20 minute watch. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬: As you move agents into production, are you seeing your bottlenecks pop up in the accelerator layer, or is it traditional compute and API latency catching you off guard? Check it out here: https://lnkd.in/gVRhCxpq Drew Bradstock Nirav Mehta Alexander Pries Jarrad Swain Daniel Newman Tom Nikl Mark Lohmeyer Amin Vahdat #AIInfra #TPUs #DynamicInfra
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Mark Lohmeyer reposted thisMark Lohmeyer reposted thisTPU Inference Performance Benchmark In the past, I mentioned that the entire Gemini model journey from pre-training, mid-training, post-training and inference is 100% on TPU. Many have asked what is the TPU's performance benchmark on major OSS models. Today, we want to showcase TPU v7 inference performance/$ on QWen3.5, as opposed to other ML hardware. This SemiAnalysis article (https://lnkd.in/gg2bkEkR) mentioned it is the first third-party inference results for TPUv7 Ironwood on InferenceX Official Preview and gave a high mark to TPU v7's performance by saying: "Google has spent more than a decade demonstrating what it can build with TPUs. Now we get to measure what the rest of the industry can do with them."; "Google has decades of software engineering experience and an extremely well established quality-driven culture, so we expect external TPU software to mature rapidly."; "TPU is King on Performance per Dollar." We will publish more benchmark result on TPU v7 and other TPU generations on other major OSS models down the road. Please stay tuned. We want TPU to be used not only as a Google internal product, but also as an external community products for non-Google AI models.
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Mark Lohmeyer shared thisGreat to see Google Cloud recognized as a clear leader in the latest Gartner Magic Quadrant for Cloud Providers, with the highest ranking for completeness of vision! We are committed to helping our customers innovate with AI and deliver at scale with great performance, reliability, and cost-effectiveness. Congrats to all the team members across Google Cloud that deliver the amazing products and innovative technologies to our customers that made this possible!Google Named a Leader in 2026 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services | Google Cloud BlogGoogle Named a Leader in 2026 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services | Google Cloud Blog
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Mark Lohmeyer reposted thisMark Lohmeyer reposted thisI am in Taiwan this week for an opportunity to speak at the Semicon 2026 conference and to celebrate our Google Taiwan office turning 20, which is home to our largest hardware engineering hub outside of the United States. To support our continued growth and meet the AI opportunity ahead, we are expanding our presence in Taipei’s Shilin District, increasing the office space footprint of our AI Infrastructure team by more than 60 percent. This expansion underscores our commitment to strengthening our partnership and engagement in Taiwan. Over the past two decades, Google's teams in Taiwan have supported our biggest platform shifts from mobile and ChromeOS to the cloud, and now AI. Today, our Taipei-based engineering hub builds on this 20-yr history by developing the physical systems needed for this work. This latest expansion is critical as we stand together at the precipice of the Age of Intelligence. Advancing intelligence is both a shared journey and a collective responsibility that requires global and industry-wide collaboration. The team in Taiwan is extremely well positioned to push our work forward along these dimensions. Happy 20th to Google Taiwan! Read our Google Taiwan blog in traditional Chinese, which also includes my recent op-ed for Business Today. For our English speakers, the Translate feature on Google Chrome is very helpful. https://lnkd.in/e4vezP3X
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Mark Lohmeyer shared thisToday, we launched powerful new FinOps controls for Gemini Enterprise to help organizations manage project-level AI spend for better tokenomics. It’s an important step forward for AI cost predictability. But as we move deeper into the agentic era, the scale of these workloads is placing new constraints at every layer of the stack, including the computing infrastructure. It’s always interesting to see what our customers are exploring to make their agentic systems more agile and cost-effective. Recently I’ve observed a lot of strategies based around something we’re calling “dynamic capacity management.” At the moment, this centers on resource scheduling and optimizing utilization, but I’m sure it’s going to expand to every corner of infrastructure in the coming months. Our latest blog provides a set of GKE and GCE capabilities to do just that. It’s well-worth a read. And if you have other techniques or capabilities you’d like us to explore, please let us know! Read the full deep dive here: https://lnkd.in/gQ3TRjWE #GoogleCloud #AIInfrastructure #FinOps #CloudArchitecture #Compute #GKEBest practices for dynamic capacity management | Google Cloud BlogBest practices for dynamic capacity management | Google Cloud Blog
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Mark Lohmeyer shared thisGreat to see the power of Google Cloud and customer choice in action!Mark Lohmeyer shared thisWhat a great way to celebrate continued AI innovation with our new partnership with Mirendil. “[Google] provides multiple kinds of chips […] this flexibility allows us to ultimately mix and match workloads with the right kind of accelerators, and then lower the cost not just for us, but also for our customers using our systems.” https://lnkd.in/g-6F4DX3Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI | TechCrunchExclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI | TechCrunch
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Mark Lohmeyer liked thisMark Lohmeyer liked thisLots of discussion out there about our next model, so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Importantly Argon has frontier safeguards and we are rolling it out responsibly - it’s with the US gov’t and going to a set of trusted cyber defenders through our Fairwind Program today. We’re going to make it available as soon as we can and as safely as we can. So hold tight, lots more coming, and you’re going to see us iterating rapidly. https://lnkd.in/gTiQb7DA
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Mark Lohmeyer liked thisMark Lohmeyer liked thisProud to have led the delivery of Hyperforce on Google Cloud. Incredible experience building a true multi-substrate platform at this scale — the engineering depth required to bring Salesforce's trust, security, and compliance standards natively onto Google Cloud's infrastructure was a massive undertaking, and I'm grateful for the chance to have led it. Huge thanks to the team and to Google Cloud for the partnership — and for the opportunity to lead this. More on the announcement: https://lnkd.in/g-sugGWCSalesforce and Google Cloud Unify Infrastructure and Agents for One-Connected AI StackSalesforce and Google Cloud Unify Infrastructure and Agents for One-Connected AI Stack
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Mark Lohmeyer liked thisThank you, Sridhar Lakshmanamurthy and Norm Jouppi, for your presentation at Hot Chips 2026 showcasing our eighth-generation TPUs. It was a fantastic representation of our TPU8 capabilities, as well as the collaborative journey and learnings that brought us here. Nicely done!Mark Lohmeyer liked thisIt was truly an honor to have the opportunity to share the stage again with the legend, Norm Jouppi, at Hot Chips 2026. We discussed the trade-offs involved in building two TPU systems this year: 8i for inference and 8t for training. A huge shout-out and thank you to the entire TPU team for your creativity, tenacity and dedication. 👏 . You rock! 🎉 🚀
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Mark Lohmeyer liked thisMark Lohmeyer liked thisSuper Thankful for Forrester and their recognition of our products and services this quarter. We talk about the the difference of Google Cloud's AI strategy be foundationally different. Being unique and providing AI optionality at all levels like the competition, but differentiate by also providing a Full Stack of ADULT ENTERPRISE grade solutions from Chips to Data, to security, to Models. This is not only better for customer choice, it just makes us better at all things AI, and continues to separate us going forward. Tech matters, services matter, approach matters (FDEs), in this nuclear paced AI race. Off we go, more to come. 🚀 🚀 🚀 Google, #ai, #LLMs, #Data, #GenAI, #technology
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Mark Lohmeyer liked thisMark Lohmeyer liked thisTPU Inference Performance Benchmark In the past, I mentioned that the entire Gemini model journey from pre-training, mid-training, post-training and inference is 100% on TPU. Many have asked what is the TPU's performance benchmark on major OSS models. Today, we want to showcase TPU v7 inference performance/$ on QWen3.5, as opposed to other ML hardware. This SemiAnalysis article (https://lnkd.in/gg2bkEkR) mentioned it is the first third-party inference results for TPUv7 Ironwood on InferenceX Official Preview and gave a high mark to TPU v7's performance by saying: "Google has spent more than a decade demonstrating what it can build with TPUs. Now we get to measure what the rest of the industry can do with them."; "Google has decades of software engineering experience and an extremely well established quality-driven culture, so we expect external TPU software to mature rapidly."; "TPU is King on Performance per Dollar." We will publish more benchmark result on TPU v7 and other TPU generations on other major OSS models down the road. Please stay tuned. We want TPU to be used not only as a Google internal product, but also as an external community products for non-Google AI models.
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