The Top 5 Cloud Trends for 2023 & Beyond

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Organisations in Asia Pacific are no longer only focused on employing a cloud-first strategy – they want to host the infrastructure and workloads where it makes the most sense; and expect a seamless integration across multiple cloud environments.

While cloud can provide the agile infrastructure that underpins application modernisation, innovative leaders recognise that it is only the first step on the path towards developing AI-powered organisations. The true value of cloud is in the data layer, unifying data around the network, making it securely available wherever it is needed, and infusing AI throughout the organisation.

Cloud provides a dynamic and powerful platform on which organisations can build AI. Pre-trained foundational models, pay-as-you-go graphics superclusters, and automated ML tools for citizen data scientists are now all accessible from the cloud even to start-ups.

Organisations should assess the data and AI capabilities of their cloud providers rather than just considering it an infrastructure replacement. Cloud providers should use native services or integrations to manage the data lifecycle from labelling to model development, and deployment.

In this Ecosystm Byte, sponsored by Oracle, Ecosystm Principal Advisor, Darian Bird presents the top 5 trends for Cloud in 2023 and beyond. Read on to find out more.

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NVIDIA to Acquire Arm: An Analysis of the Biggest Tech Deal of 2020

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Last week, NVIDIA announced that it had agreed to acquire UK-based chip company Arm from Japanese conglomerate SoftBank in a deal estimated to be worth USD 40 billion. In 2016, SoftBank had acquired Arm for USD 32 billion. The deal is set to unite two major chip companies; power data centres and mobile devices for the age of AI and high-performance computing; and accelerate innovation in the enterprise and consumer market.

Rationale for the Deal

NVIDIA has long been the industry leader in graphics chips (GPUs), and a smaller but significantly profitable player in the chip stakes. With graphic processing being a key component in AI applications like facial recognition, NVIDIA was quick to capitalise. This allowed it to move into data centres – an area long dominated by Intel who still holds the lion’s share of this market. NVIDIA’s data centre business has grown tremendously – from near zero less than ten years ago to nearly USD 3 billion in the first two quarters of this fiscal year. It contributes 42% of the company’s total sales.

The gaming PC market has been the fastest-growing segment in the PC market. The rare shining light in an otherwise stagnant-to-slightly declining market. NVIDIA has benefited greatly from this with a huge jump in their graphics revenues. Its GeForce brand is one of the most desired in the industry. However, with their success in AI, NVIDIA’s ambition has now grown well beyond the graphics market. Last year NVIDIA acquired Mellanox – who makes specialised networking products especially in the area of high-performance computing, data centres, cloud computing – for almost USD 7 billion. There is clearly a desire to expand the company’s footprint and position itself as a broad-based player in the data centre and cloud space focused on AI computing needs.

The acquisition of Arm though adds a whole new dimension. Arm is the leading technology provider in the mobile chip market. A staggering 90% of smartphones are estimated to use Arm technology. Arm is the colossus of the small chip industry – having crossed 20 billion in unit shipments in 2019.

Acquiring Arm is likely to result in NVIDIA now having a play in the effervescent smartphone market. But the company is possibly eyeing a different prize. Jensen Huang, Founder and CEO of NVIDIA said “AI is the most powerful technology force of our time and has launched a new wave of computing. In the years ahead, trillions of computers running AI will create a new internet-of-things that is thousands of times larger than today’s internet-of-people. Our combination will create a company fabulously positioned for the age of AI.”

With thoughts of self-driving cars, connected homes, smartphones, IoT, edge computing – all seamlessly working with each other, the acquisition of Arm provides NVIDIA a unique position in this market. As the number of connected devices explodes, as many billions of sensors become an ubiquitous part of 21st century living, there is going to be a huge demand for low power processing everywhere. Having that market may turn out to be a larger prize than the smartphone market. The possibilities are endless.

While this deal is supposed to be worth around USD 40 billion, somewhere between USD 23-28 billion is going to be paid in the form of NVIDIA stock. This brings us to an extremely interesting dynamic. At the beginning of 2016 NVIDIA’s market cap was less than USD 20 billion. Mighty Intel was at USD 150 billion. AMD the other player in the market for chips who also sell graphics was at a mere USD 2 billion. In July this year, NVIDIA’s value passed Intel’s and today it is sitting at around USD 300 billion! Intel with a recent dip is now close to USD 200 billion. AMD too with all the tech-fueled growth in recent years has grown to just shy of USD 100 billion market cap.

NVIDIA Growth 2014-2021

What this tells us is that the stock portion of the deal is cheaper for NVIDIA today by around 55% compared to if this deal was consummated on 1st January 2020. If there was a right time for NVIDIA to buy – it is now. This also shows the way the company has grown revenue at a massive clip powered by Gaming PCs and AI. The deal to buy Arm appears to be a very good idea, which would establish NVIDIA as a leader in the chip industry moving forward.

Ecosystm Comments

While there appears to be some good reasons for this deal and there are some very exciting possibilities for both NVIDIA and Arm, there are some challenges.

The tech industry is littered with examples of large mergers and splits that did not pan out. Given that this is a large deal between two businesses without a large overlap, this partnership needs to be handled with a great deal of care and thought. The right people need to be retained. Customer trust needs to be retained.

Arm so far has been successful as a neutral provider of IP and design. It does not make chips, far less any downstream products. It therefore does not compete with any of the vendors licensing its technology. NVIDIA competes with Arm’s customers. The deal might create significant misgivings in the minds of many customers about sharing of information like roadmaps and pricing. Both companies have been making repeated statements that they will ensure separation of the businesses to avoid conflicts.

However, it might prove to be difficult for NVIDIA and Arm to do the delicate dance of staying at arm’s length (pun intended) while at the same time obtaining synergies. Collaborating on technology development might prove to be difficult as well, if customer roadmaps cannot be discussed.

Business today also cannot escape the gravitational force of geo-politics. Given the current US-China spat, the Chinese media and various other agencies are already opposing this deal. Chinese companies are going to be very wary of using Arm technology if there is a chance the tap can be suddenly shut down by the US government. China accounts for about 25% of Arm’s market in units. One of the unintended consequences which could emerge from this is the empowerment of a new competitor in this space.

NVIDIA and Arm will need to take a very strategic long-term view, get communication out well ahead of the market and reassure their customers, ensuring they retain their trust. If they manage this well then they can reap huge benefits from their merger.


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Nvidia and Intel Race For The Future Of Machine Learning

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4/5 (4) Two things happened recently that 99% of the ICT world would normally miss. After all microprocessor and chip interconnect technology is quite the geek area where we generally don’t venture into. So why would I want to bring this to your attention?

We are excited about the innovation that analytics, machine learning (ML) and all things real time processing will bring to our lives and the way we run our business. The data center, be it on an enterprise premise or truly on a cloud service provider’s infrastructure is being pressured to provide compute, memory, input/output (I/O) and storage requirements to take advantage of the hardware engineers would call ‘accelerators’. In its most simple form, an accelerator microprocessor does the specialty work for ML and analytics algorithms while the main microprocessor is trying to hold everything else together to ensure that all of the silicon parts are in sync. If we have a ML accelerator that is too fast with its answers, it will sit and wait for everyone else as its outcomes squeezed down a narrow, slow pipe or interconnect – in other words, the servers that are in the data center are not optimized for these workloads. The connection between the accelerators and the main components becomes the slowest and weakest link…. So now back to the news of the day.

A new high speed CPU-to-device interconnect standard, the Common Express Link (CXL) 1.0 was announced by Intel and a consortium of leading technology companies (Huawei and Cisco in the network infrastructure space, HPE and Dell EMC in the server hardware market, and Alibaba, Facebook, Google and Microsoft for the cloud services provider markets). CXL joins a crowded field of other standards already in the server link market including CAPI, NVLINK, GEN-Z and CCIX. CXL is being positioned to improve the performance of the links between FPGA and GPUs, the most common accelerators to be involved in ML-like workloads.

Of course there were some names that were absent from the launch – Arm, AMD, Nvidia, IBM, Amazon and Baidu. Each of them are members of the other standards bodies and probably are playing the waiting game.

Now let’s pause for a moment and look at the other announcement that happened at the same time. Nvidia and Mellanox announced that the two companies had reached a definitive agreement under which Nvidia will acquire Mellanox for $6.9 billion.  Nvidia puts the acquisition reasons as “The data and compute intensity of modern workloads in AI, scientific computing and data analytics is growing exponentially and has put enormous performance demands on hyperscale and enterprise datacenters. While computing demand is surging, CPU performance advances are slowing as Moore’s law has ended. This has led to the adoption of accelerated computing with Nvidia GPUs and Mellanox’s intelligent networking solutions.”

So to me it seems that despite Intel working on CXL for four years, it looks like they might have been outbid by Nvidia for Mellanox. Mellanox has been around for 20 years and was the major supplier of Infiniband, a high speed interconnect that is common in high performance workloads and very well accepted by the HPC industry. (Note: Intel was also one of the founders of the Infiniband Trade Association, IBTA, before they opted to refocus on the PCI bus). With the growing need for fast links between the accelerators and the microprocessors, it would seem like Mellanox persistence had paid off and now has the market coming to it. One can’t help but think that as soon as Intel knew that Nvidia was getting Mellanox, it pushed forward with the CXL announcement – rumors that have had no response from any of the parties.

Advice for Tech Suppliers:

The two announcements are great for any vendor who is entering the AI, intense computing world using graphics and floating point arithmetic functions. We know that more digital-oriented solutions are asking for analytics based outcomes so there will be a growing demand for broader commoditized server platforms to support them. Tech suppliers should avoid backing or picking one of either the CXL or Infiniband at the moment until we see how the CXL standard evolves and how nVidia integrates Mellanox.

Advice for Tech Users:

These two announcements reflect innovation that is generally so far away from the end user, that it can go unnoticed. However, think about how USB (Universal Serial Bus) has changed the way we connect devices to our laptops, servers and other mobile devices. The same will true for this connection as more and more data is both read and outcomes generated by the ‘accelerators’ for the way we drive our cars, digitize our factories, run our hospitals, and search the Internet. Innovation in this space just got a shot in the arm from these two announcements.

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