How ASUS Server and Infrastructure Solutions are Solving the Biggest AI Challenges Facing Organisations Today


Report How ASUS Server and Infrastructure Solutions are Solving the Biggest AI Challenges Facing Organisations Today Print Page Download PDF AI IS CHANGING THE GAME; BUT FOR SOME, INNOVATION IS STILTED AI has fast become a part of our vocabulary, not just in business but in our everyday lives, too. It’s unsurprising, given its magic-wand appeal to enable more output for less effort in less time. Streamlining tasks. Boosting productivity. Enabling more informed decisions based on accurate, predictive insights. It’s personalising experiences. Bringing innovative ideas to life – at speed. In manufacturing, anomalies can be detected on the production line before they affect the end customer. In transportation, AI can process sensor data and make critical decisions in real time for autonomous vehicles. And in healthcare we’re seeing AI speed up the diagnosis processes, AI IS CHANGING THE GAME; BUT FOR SOME, INNOVATION IS STILTED such as screening for lung cancer,1 so patients receive life-saving treatment earlier. But, with innovation comes responsibility. And while AI can very nearly do it all, it does come with significant hurdles; to unleash the full potential of AI, huge processing power is required. Without a clear strategy and effective solution from the outset, organisations will be faced with AI deployments that can’t fulfil their potential. This not only affects the organisation, but those its services reach, meaning end-users might not benefit from life-altering scientific advancements, for example, in time. YET AI IS STILL EVOLVING, AND PROCESSING REQUIREMENTS ARE GROWING AI is moving at a rate that’s hard to keep pace with. The technology needs a complex infrastructure of software and servers to enable fast access to huge volumes of data to enable intricate tasks – from machine learning and deep learning (a machine learning technique that uses vast amounts of data to build layers of understanding), to large language models (LLMs). Then there’s the rise of generative AI (GenAI), with McKinsey Global Institute estimating that GenAI will add between $2.6 and $4.4 trillion in annual value to the global economy (increasing the economic impact of AI by 15-40%). The firm also projects that AI will automate half of all work by 2040 – 2060, with GenAI pushing that window a decade earlier than previous estimates.4 Open source LLMs are also coming into play, like Meta AI’s Llama 3.2. 5 And while these increase transparency, customisation and cost-effectiveness compared with closed-source alternatives, they require increased expertise in model management, maintenance, governance and hardware infrastructure instead. With all this (and more than we can fit here) happening in the AI arena, it’s no surprise that the computing power required for AI is doubling every 100 days and is projected to increase by more than a million times over the next five years.6 Keeping up with such rapid increases in computational capacity requirements is a challenge, and one that’s seeing the rise of supercomputers, to help maximise the impact of AI. The processing challenges that must be addressed to fully unlock the potential of AI for all organisations: Eliminating the latency that can compromise increasingly complex LLM, ML, and DL models and techniques, and addressing the need for exceptionally high-performance GPU compute Accommodating the move of data back on-premises as organsations consider the security implications of keeping confidential data in the cloud Managing high volumes of data and moving processing to the Edge as organisations move to decrease latency and bring better performance to the user Focus on resiliency of high-performance compute for the real-time decisioning nature of AI applications HOW SUPERCOMPUTING IS REVOLUTIONISING AI The supercomputer has become a critical solution for many industries shaping a future in AI – across research and academia, from healthcare to sciences and biotechnology. There are more of these hugely powerful devices than people realise, with the three most powerful based in the United States7 , but hundreds more existing and growing in number across the globe. AI supercomputers mark a new era of computing – simplifying programming and supporting the larger memory requirements of AI tasks. Made up of countless processors and finely tuned hardware, AI supercomputers provide fast, scalable computing power, ample storage, and secure networking – easily overcoming the afore-mentioned challenges of AI. Businesses are already seeing these processing giants support the delivery of real-time and convenient computing services across industries; and by enabling heavy AI workloads the real-world applications are far reaching, from climate prediction and molecular model simulation all the way to engineering design and simulation BUILDING BESPOKE SOLUTIONS FIT FOR THE AI FUTURE The power of supercomputers and what they can offer is obvious, overcoming many processing hurdles such as latency. With the right partner supporting the design of supercomputers, customers can access a comprehensive AI infrastructure built for diverse workloads and the dynamic needs that comes with deploying AI solutions – enabling even the most ambitious vision. It’s essential that both the hardware and software that organisations choose to adopt as part of their AI journey are purpose-built for handling intensive AI tasks. And as we’ve already discussed, those tasks are set to get more complicated and require increasing amounts of processing power, so they need to be ready for not just today, but tomorrow’s AI needs too. This requires a software-driven approach that ensures seamless operations, speeding AI deployments so organisations can efficiently process vast datasets and execute complex computations without worry. At ASUS we consider the needs of AI across every aspect of our portfolio, from the Edge to the server and beyond. Customers can feel confident that they’ll get the very most out of their AI deployments with us. ASUS can offer: • AI capabilities embedded from the Edge to the server and beyond • Quick fulfilment response to almost any requirement, with top-tier components, strong ecosystem partnerships, feature-rich designs, and superior in-house expertise \ • A complete AI server solution from software to hardware • AI expertise includes leveraging internal software whilst partnerships with software and cloud providers offer holistic solutions FROM STRENGTH TO STRENGTH: OUR MOST POWERFUL AI SERVER YET The ASUS ESC N8-E11/E11V AI server is designed for generative AI with optimised server systems, data-centre infrastructure, and AI software-development capabilities. Powered by 7U NVIDIA®’s HGX H100/H200 eight GPU server AI accelerators, the ESC N8-E11 and E11v are built to deliver robust AI computing capabilities where they’re needed most. Giving organisations: • The full power of NVIDIA GPUs, BlueField-3, NVLink, NVSwitch, and networking • Efficient scaling with direct GPU-to-GPU interconnect via NVLink delivering 900GB/s bandwidth • The highest throughput during computerintensive workloads • Modular design with reduced cable usage • High-level power efficiency: 4 + 2 80 PLUS Titanium power supplies • Optimised thermal design to support efficiency goals Clever cooling capabilities: Optimised power efficiency and thermal design means the ESC N8-E11/E11V feature both air cooling and direct-to-chip (D2C) liquid cooling solutions, in addition to dedicated CPU and GPU airflow tunnels to expel heat, reducing operational cost and ensuring peak performance at all times. Liquid cooling: Traditional methods of cooling struggle to cope with the heat generated by dense clusters of CPUs and GPUs, impacting on data centre efficiency. Liquid cooling is the energy-efficient solution that can scale with cluster density and processing loads\ Direct Liquid Cooling (DLC) Direct to Chip (D2C): A cold plate directly on top of CPUs and GPUs continually funnels heat through a liquid coolant network to a cooling distribution unit (CDU) in the rack. The CDU dissipates the heat, circulating the chilled coolant back in a closed-loop system.\ Immersion cooling: This involves submerging an entire server into a thermally conductive dielectric fluid for maximum heat dissipation. It is regarded as the most energy-efficient form of liquid cooling on the market.

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How ASUS Server and Infrastructure Solutions are Solving the Biggest AI Challenges Facing Organisations Today

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AI IS CHANGING THE GAME; BUT FOR SOME, INNOVATION IS STILTED

AI has fast become a part of our vocabulary, not just in business but in our everyday lives, too. It’s unsurprising, given its magic-wand appeal to enable more output for less effort in less time. Streamlining tasks. Boosting productivity. Enabling more informed decisions based on accurate, predictive insights. It’s personalising experiences. Bringing innovative ideas to life – at speed. In manufacturing, anomalies can be detected on the production line before they affect the end customer. In transportation, AI can process sensor data and make critical decisions in real time for autonomous vehicles. And in healthcare we’re seeing AI speed up the diagnosis processes, AI IS CHANGING THE GAME; BUT FOR SOME, INNOVATION IS STILTED such as screening for lung cancer,1 so patients receive life-saving treatment earlier. But, with innovation comes responsibility. And while AI can very nearly do it all, it does come with significant hurdles; to unleash the full potential of AI, huge processing power is required. Without a clear strategy and effective solution from the outset, organisations will be faced with AI deployments that can’t fulfil their potential. This not only affects the organisation, but those its services reach, meaning end-users might not benefit from life-altering scientific advancements, for example, in time.

YET AI IS STILL EVOLVING, AND PROCESSING REQUIREMENTS ARE GROWING

AI is moving at a rate that’s hard to keep pace with. The technology needs a complex infrastructure of software and servers to enable fast access to huge volumes of data to enable intricate tasks – from machine learning and deep learning (a machine learning technique that uses vast amounts of data to build layers of understanding), to large language models (LLMs).

Then there’s the rise of generative AI (GenAI), with McKinsey Global Institute estimating that GenAI will add between $2.6 and $4.4 trillion in annual value to the global economy (increasing the economic impact of AI by 15-40%). The firm also projects that AI will automate half of all work by 2040 – 2060, with GenAI pushing that window a decade earlier than previous estimates.4 Open source LLMs are also coming into play, like Meta AI’s Llama 3.2. 5 And while these increase transparency, customisation and cost-effectiveness compared with closed-source alternatives, they require increased expertise in model management, maintenance, governance and hardware infrastructure instead.

With all this (and more than we can fit here) happening in the AI arena, it’s no surprise that the computing power required for AI is doubling every 100 days and is projected to increase by more than a million times over the next five years.6 Keeping up with such rapid increases in computational capacity requirements is a challenge, and one that’s seeing the rise of supercomputers, to help maximise the impact of AI.

The processing challenges that must be addressed to fully unlock the potential of AI for all organisations:

Eliminating the latency that can compromise increasingly complex LLM, ML, and DL models and techniques, and addressing the need for exceptionally high-performance GPU compute
Accommodating the move of data back on-premises as organsations consider the security implications of keeping confidential data in the cloud
Managing high volumes of data and moving processing to the Edge as organisations move to decrease latency and bring better performance to the user
Focus on resiliency of high-performance compute for the real-time decisioning nature of AI applications

HOW SUPERCOMPUTING IS REVOLUTIONISING AI

The supercomputer has become a critical solution for many industries shaping a future in AI – across research and academia, from healthcare to sciences and biotechnology. There are more of these hugely powerful devices than people realise, with the three most powerful based in the United States7 , but hundreds more existing and growing in number across the globe.

AI supercomputers mark a new era of computing – simplifying programming and supporting the larger memory requirements of AI tasks. Made up of countless processors and finely tuned hardware, AI supercomputers provide fast, scalable computing power, ample storage, and secure networking – easily overcoming the afore-mentioned challenges of AI.

Businesses are already seeing these processing giants support the delivery of real-time and convenient computing services across industries; and by enabling heavy AI workloads the real-world applications are far reaching, from climate prediction and molecular model simulation all the way to engineering design and simulation

BUILDING BESPOKE SOLUTIONS FIT FOR THE AI FUTURE

The power of supercomputers and what they can offer is obvious, overcoming many processing hurdles such as latency. With the right partner supporting the design of supercomputers, customers can access a comprehensive AI infrastructure built for diverse workloads and the dynamic needs that comes with deploying AI solutions – enabling even the most ambitious vision.

It’s essential that both the hardware and software that organisations choose to adopt as part of their AI journey are purpose-built for handling intensive AI tasks. And as we’ve already discussed, those tasks are set to get more complicated and require increasing amounts of processing power, so they need to be ready for not just today, but tomorrow’s AI needs too. This requires a software-driven approach that ensures seamless operations, speeding AI deployments so organisations can efficiently process vast datasets and execute complex computations without worry.

At ASUS we consider the needs of AI across every aspect of our portfolio, from the Edge to the server and beyond. Customers can feel confident that they’ll get the very most out of their AI deployments with us.

ASUS can offer:

• AI capabilities embedded from the Edge to the server and beyond

• Quick fulfilment response to almost any requirement, with top-tier components, strong ecosystem partnerships, feature-rich designs, and superior in-house expertise \

• A complete AI server solution from software to hardware

• AI expertise includes leveraging internal software whilst partnerships with software and cloud providers offer holistic solutions

FROM STRENGTH TO STRENGTH: OUR MOST POWERFUL AI SERVER YET

The ASUS ESC N8-E11/E11V AI server is designed for generative AI with optimised server systems, data-centre infrastructure, and AI software-development capabilities. Powered by 7U NVIDIA®’s HGX H100/H200 eight GPU server AI accelerators, the ESC N8-E11 and E11v are built to deliver robust AI computing capabilities where they’re needed most. Giving organisations:

• The full power of NVIDIA GPUs, BlueField-3, NVLink, NVSwitch, and networking

• Efficient scaling with direct GPU-to-GPU interconnect via NVLink delivering 900GB/s bandwidth

• The highest throughput during computerintensive workloads • Modular design with reduced cable usage

• High-level power efficiency: 4 + 2 80 PLUS Titanium power supplies

• Optimised thermal design to support efficiency goals

Clever cooling capabilities: Optimised power efficiency and thermal design means the ESC N8-E11/E11V feature both air cooling and direct-to-chip (D2C) liquid cooling solutions, in addition to dedicated CPU and GPU airflow tunnels to expel heat, reducing operational cost and ensuring peak performance at all times.
Liquid cooling: Traditional methods of cooling struggle to cope with the heat generated by dense clusters of CPUs and GPUs, impacting on data centre efficiency. Liquid cooling is the energy-efficient solution that can scale with cluster density and processing loads\
Direct Liquid Cooling (DLC) Direct to Chip (D2C): A cold plate directly on top of CPUs and GPUs continually funnels heat through a liquid coolant network to a cooling distribution unit (CDU) in the rack. The CDU dissipates the heat, circulating the chilled coolant back in a closed-loop system.\
Immersion cooling: This involves submerging an entire server into a thermally conductive dielectric fluid for maximum heat dissipation. It is regarded as the most energy-efficient form of liquid cooling on the market.