Unlocking the full potential of AI is at the heart of data centre demand right now, whether it’s a cloud model or with on-premises infrastructure. But where does High Performance Computing (HPC) fit in, and what does the future look like for computing at scale?
TBT Marketing recently attended ISC in Germany, Europe’s largest HPC event, and there was one thing on exhibitor and attendee minds… generative AI — how to develop solutions fast, for a variety of use cases, and at an affordable cost. There’s an insatiable thirst for all things Gen AI right now, and the race is on for domination on-premises and in the cloud.
As we’ve discussed a lot recently, generative AI refers to the ability of machines to create original content, such as images, music, or text, using deep learning techniques. These AI models are trained on vast amounts of data and possess the capacity to generate realistic and innovative outputs. However, training and deploying generative AI models require significant computational resources, which is where HPC finds its home.
How to HPC for Gen AI
Traditionally, organisations have relied on on-premises HPC infrastructure to develop and deploy AI solutions in-house. These setups involve dedicated clusters of high performance servers equipped with powerful GPUs and parallel processing capabilities. On-premises HPC provides organisations with full control over their computing resources, data privacy, and low-latency access, making it an attractive choice for sensitive industries like finance and healthcare.
In recent years, the advent of cloud computing has revolutionised the way businesses approach HPC. Public cloud-based HPC solutions enable organisations to access vast computational resources on-demand and at scale, eliminating the need for upfront infrastructure investment. This scalability, flexibility, and cost-effectiveness makes cloud-based HPC an appealing option for industries now seeking greater agility and rapid development cycles.
How HPC advances Gen AI solutions
Generative AI has the potential to revolutionise so many industries, and not just creative ones. It’s already being used heavily in healthcare and financial services for example. A report just published from McKinsey & Company predicts that if implemented correctly the technology could deliver value equal to an additional $200-$340 billion annually. And when it comes to labour productivity across mature economies, by 2040 we could be looking at adding more than 3% annually to growth (if rates of adoption and training keep pace).
Let’s dig a little deeper into how industries are leveraging HPC infrastructure for generative AI to drive innovation and address complex challenges.
In the financial services industry, HPC-powered generative AI models are already having a big impact on algorithmic trading, risk management, fraud detection, and portfolio optimisation. By analysing vast amounts of historical data, these models can generate realistic market scenarios, identify patterns, and develop sophisticated trading strategies, leading to improved decision-making and reduced financial risks.
Telecoms providers are using generative AI and HPC to optimise network design, enhance customer experiences by building better products, and predict network failures. By analysing massive datasets, these AI models can generate synthetic network topologies, predict demand patterns, and optimise network configurations — ultimately leading to more efficient and reliable communication networks.
HPC combined with generative AI is empowering the cyber security industry to combat emerging threats and identify vulnerabilities. AI models can analyse large volumes of network traffic, detect anomalies, and generate synthetic attack scenarios to strengthen defences and help safeguard critical systems and data.
In healthcare and life sciences areas, HPC and generative AI is particularly prevalent in drug discovery, genomics, and medical research. AI models can look at vast biological datasets, generate novel molecular structures, and simulate drug interactions to accelerate the development of new therapeutics and treatments. This approach has the potential to significantly reduce research and development timelines, saving lives and improving patient care.
New model army
To unlock the full potential of generative AI, organisations rely on Large Language Models (LLMs) and Foundational Models (FMs). These models, such as OpenAI’s GPT‑3, provide a foundation for building innovative and context-aware AI solutions across various industries. LLMs can generate human-like text, answer complex queries, and facilitate natural language understanding, enabling seamless interactions between humans and machines. Cloud giants like Amazon Web Services (AWS), Google Cloud and Microsoft Azure are rapidly developing their solutions to aide developers in getting their Gen AI products off the ground more quickly (as cloud resources can be used almost instantly).
Then there’s effective AI data management, another big topic at this year’s ISC conference, and one that’s seeing a flurry of new companies launch to meet the need. Data management is crucial for organisations using generative AI at scale. With cloud and on-premises solutions, businesses can securely store, process, and analyse massive amounts of data required for training and fine-tuning generative AI models into the future.
The demand is now
For years HPC has been converging with AI & ML, but the rise of generative AI will see a spike in demand for more on-premises and cloud solutions. By leveraging LLMs and FMs, and implementing robust AI data management practices, businesses can unlock new opportunities for innovation, enhance decision-making, and tackle today’s complex scientific and business challenges… it’s an exciting time for vendors and HPC practitioners alike.
At TBT Marketing, we have over 20 years of experience in enterprise IT and have witnessed first hand the rise of machine learning and AI, and how it has been marketed to the developer community. Get in touch to see how we can help your business.