Reprint The second wave of AI: Inference and optimization of pre-training models
in the first wave of AI application, early explorers focused on developing and training basic models, laying the foundation for revolutionary AI capabilities. However, the focus will shift in 2025. We are stepping into the second wave of AI applications, and reasoning and fine-tuning of pre-training models will become the core. Enterprises will increasingly tend to use existing models as customizable tools instead of investing a lot of time and resources to build new models from scratch.
This trend stems from practical considerations for enterprises to accelerate AI ROI. The pre-training model is essentially a customizable template that enables enterprises to adjust it to specific application scenarios with minimal effort. The focus will be on quickly and efficiently converting massive amounts of raw data into executable insights. Fine-tuning for specific fields (such as medical, financial, retail and other industries) will enable enterprises to tap value faster and promote the realization of business results on a large scale.
For IT leaders, this means that the AI strategy needs to be re-examined. The focus of AI infrastructure will shift from supporting model training to infrastructure that optimizes inference workloads. A high-performance and scalable system that can process these AI pipelines with low latency will become the key to success.
Energy is currency: Redefining AI economy by energy density
with AI reshaping all walks of life, energy has become the core "currency" of this new era ". Training and running AI workloads requires huge computing power, which in turn turns into huge energy requirements. However, the surge in global AI data centers has surpassed the existing energy supply, which has brought key bottlenecks to many enterprises.
In 2025, energy efficiency will become the decisive element of AI economy. Enterprises that can maximize the energy efficiency of data centers and choose cloud service providers that adhere to the concept of sustainable development will be able to reduce the pressure of AI on the power grid while achieving higher AI output. This will promote investment in the following areas:- high performance hardware design: hardware for optimizing the energy efficiency ratio will become crucial. High-performance GPU, DPU, and CPU are necessary for AI operations.
- Innovative Cooling Solutions: liquid cooling emerging technologies such as immersion cooling will help reduce energy consumption while maintaining high performance.
- Integration of renewable energy: enterprises will apply renewable energy and explore strategies such as carbon credit to offset the impact of AI operations on the environment.
Energy efficiency is not only a problem of cost control, but also an embodiment of competitive advantage. Enterprises that can efficiently expand AI workloads and minimize energy use will take the lead in an increasingly energy-intensive world.
Meeting E-level computing: future data infrastructure construction
e-level Computing (Exascale Computing)-- At least 3.2 billion computations per second (1 exaflop)-- was once regarded as an unattainable target. However, this will become a reality in 2025. We have experienced this change: At the beginning of 2024, we did not have any e-level computing customers, but now, at the end of the year, we have many customers, one of the enterprises has managed nearly 10EB of data.
Although e-level computing has not been popularized to all enterprises at present, 2025 will be a year for more and more enterprises to take it into consideration. Enterprises entering this unknown field will face unique challenges, including managing large data sets and ensuring the scalability and reliability of infrastructure.
For IT leaders, preparing for the e-level computing era means bold investments in storage, computing, and network technology. It is crucial to establish partnerships with suppliers who have a deep understanding of e-level computing features and develop solutions designed for large amounts of data and complexity.
The experience and lessons of early adopters will lay a solid foundation for wide adoption in the next few years. Those enterprises that dare to meet the e-level computing challenges now will take the lead in the future data-driven economy.
The rise of DPU: A Revolutionary driving force for infrastructure efficiency
in 2025, DPU will become the core component of IT infrastructure, marking a key turning point in the development of DPU. These powerful processors, such as NVIDIA's BlueField-3, aim to uninstall key tasks such as network, storage and security from CPU and GPU, thus making the overall operation of the system more efficient.
The rapid growth of AI workloads, cloud-native applications, and distributed systems has driven this shift. In order to meet the performance requirements of low latency and high throughput, enterprises urgently need a solution that can improve scalability and reduce energy consumption, and DPU came into being.
The importance of DPU will become more and more prominent in 2025, mainly due to the following reasons:- optimize the AI pipeline: by processing peripheral tasks and releasing CPU and GPU resources, DPU can focus on core AI tasks to maximize efficiency.
- Support for distributed systems: with the increasing number of distributed applications deployed by enterprises, DPU provides the excellent performance and scalability required to manage these workloads.
- Security of reinforcement: DPU provides hardware-based isolation and uninstallation for security tasks, significantly enhancing the overall toughness of the system.
For IT leaders, 2025 is a key year to integrate DPU into infrastructure. Those enterprises that take the lead in applying this technology will gain significant advantages in performance optimization and energy utilization.
Make full preparations for 2025
- enhance AI inference capabilities first: optimize the infrastructure to better support inference workloads and ensure efficient use of the value of the pre-training model.
- Invest in energy-saving technologies: assess the energy use of the data center and its cloud strategy, and explore various solutions from innovative cooling technologies to renewable energy integration to maximize energy efficiency.
- Plan E-level computing: even if e-level computing has not been incorporated into the company's recent planning, it should lay a solid foundation for managing larger data sets and expanding infrastructure.
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Use DPU to perform key tasks: try to use DPU to uninstall tasks to improve the running efficiency of AI and cloud-native applications.
By focusing on these key areas, enterprises will be able to occupy a favorable position in the rapidly changing IT environment and make full use of various opportunities in 2025.
Conclusion
predicting the future is always a delicate combination of imagination and insight. Although not all predictions will be realized as scheduled, the trend in 2025 has gradually become clear. The second wave of AI application focuses on reasoning, energy efficiency becomes a competitive advantage, the rise of e-level computing and the wide adoption of DPU are not only predictions, but also development tracks that have been accelerated.
We are committed to helping enterprises properly cope with these changes through cutting-edge solutions. We have future-oriented technological advances, including cloud-native architectures, data platforms that support e-level computing, and full support for next-generation hardware such as DPU and GPU, ensure that enterprises can achieve seamless expansion while maintaining high performance and efficiency. Following these trends will help enterprises fully release the potential of IT investment and flourish in a changing world with AI as its core.
----------references: Ben David, Shimon. "Shaping the AI Future: WEKA's Top IT Predictions for 2025." WEKA, December 20,2024. Accessed January 23,2025. https://www.weka.io/blog/ai-ml/shaping-the-ai-future-wekas-top-it-predictions-for-2025.
