Meade Engineering, Inc.’s Post

This phase is interesting in the evolution of Data Centers - AI work loads - The demand for GPU - Cooling technologies - Sustainability It is a trillion dollar market; how you address these will set you apart from the rest.

Moving infrastructure to the edge continues to grow at a rate two times faster than core infrastructure. AI requires proximity to its data and traditional infrastructure and network architecture likely won't support it. Even if you don't have immediate plans for AI as a business, it's important to look at your infrastructure and network and ask if it can support AI when it inevitably has to. Check out this article to learn more about the bright future of AI, cloud, network, and data: #artificialintelligence #cloud #network #data #digitaltransformation https://lnkd.in/g6kHPfgY

According to the latest research from Synergy Research Group, the hyperscale data center industry has reached a major milestone - over 1,000 large data centers globally operated by major cloud and internet companies. This is an incredible achievement when you consider that just 10 years ago in 2013, there were only around 300 of these massive data centers worldwide. The industry has more than tripled in scale in a single decade. A few key highlights that underscore this exponential growth: 💻 Total global hyperscale data center capacity is doubling every 4 years to keep up with skyrocketing demand for cloud services. The U.S. amazingly still accounts for 51% of worldwide hyperscale capacity, though other regions like Europe and China are rapidly expanding. 🤖 AI is being cited as a "prime reason" for the increasing massive scale of new data center builds as companies double down on infrastructure for AI/ML workloads. 🥇 The leading cloud providers - Amazon Web Services (AWS), Microsoft, and Google - now account for 60% of all hyperscale capacity as they race to meet soaring demand. The data center industry's torrid 10-year growth pace looks to only accelerate further as transformative technologies like AI go mainstream and drive ever-higher data and compute needs.

Excellent article by Grant Gross defining the problem that all enterprise, service provider and cloud providers will face. https://lnkd.in/gwepjABx #HybridAI #poweroprimization #costoptimization

Hyperscale data centers have hit 1,000 globally, with the U.S. leading the charge with 51% of global capacity. The race towards AI dominance is evident as cloud giants like Amazon, Microsoft, and Google expand their data center footprints. Synergy Research forecasts a doubling of total hyperscale data center capacity in the next four years, driven by the increasing scale of new facilities and AI technology. The future of data centers is looking bigger and smarter than ever before. Read more in this article from Data Center Frontier. #Hyperscale #DataCenters #AI #CloudGiants https://lnkd.in/gBbmNVaR

Edge adoption and innovation continues to accelerate. We are seeing the momentum driven by the need for a better customer experience, reducing cloud & networking costs, and significant latency benefits. #AI is just one the many usecases... but they all require rethinking how to handle the massive amounts of data in highly distributed locations. If you are a developer or architect join the thousands already implementing NATS.io to interconnect workloads across clouds and extend to the #edge - https://lnkd.in/equXYpsZ Synadia #RAG #golang #rust #opensource

On-premises storage, infrastructure evolves to keep pace with AI Pure Storage is the latest infrastructure vendor to add Nvidia DGX SuperPod certification and new product offerings to support generative AI workloads. The era of artificial intelligence is upon us, and infrastructure providers have responded by updating their systems to support generative AI workloads. In an August 2023 research study conducted by TechTarget's Enterprise Strategy Group, 54% of respondents said they expect to have a generative AI initiative in place in the next 12 months. With AI, like many new or emerging workloads that require a level of infrastructure modernization, the initial expectation is that many organizations will turn to public cloud services. The value that AI delivers, however, is tied to the quality of the data it utilizes. For the modern enterprise, data is everywhere: in the cloud, on premises and at the edge. As the exuberance surrounding the business potential of AI starts to stabilize, organizations are shifting their focus to data security, privacy and locality considerations. Pure Storage NVIDIA https://lnkd.in/gTVJzRmg

New on Runtime: The AI training boom has upended nearly everything in enterprise tech, and that extends to the data centers that provide the foundational layer for the entire thing. Decades of assumptions about the best way to design the massive buildings are shifting as new workloads take precedence, but the buildout is also happening at a time when a lot of experts believe AI inference will eventually become the most important part of the enterprise IT stack. Here are some of the components of modern data centers that are rapidly changing amid the AI boom, as explained by the people responsible for keeping up with those changes. Thanks to Amazon Web Services (AWS)'s Prasad Kalyanaraman, Google Cloud's Partha Ranganathan, Equinix's Tiffany Thoms Osias and Marvell Technology's Radha Nagarajan for their thoughts and insights into this huge shift.

Generative AI is expected to create a second stream of data center demand comparable to the cloud. Here's a look at the role of the colocation data center in helping meet this historic wave of new demand, and how data center operators can be ready to capitalize.

Expect AI to drive hyperscale datacenter growth.