How inference will reshape AI infrastructure and compute demand

The GPU market is dead, long live the GPU market

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If you work in tech you might relate to the strange time paradox that exists here: time passes strangely and a day can feel like it passes in hours while the distance between weeks can feel like months. Two weeks ago we posted a blog sharing our perspective on the impact of DeepSeek R1 on demand for AI compute. (In summary, we believe models like R1 will ultimately increase the demand for AI infrastructure.) Fast forward to today and the concern about the declining need for AI compute seems to have faded away.

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A recent article from the Financial Times looks at shifts in the AI industry and how the types of workloads being deployed will impact the demand for compute over the next two years. As more models are trained and then used to power inference workloads, we could see a dramatic shift in not only the need for compute capacity but also in the chip space where NVIDIA currently dominates but companies like Meta are working to develop their own specialized AI chips.

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The ‘agentic’ impact

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According to the Financial Times, "inference computing now requires increased computational power when users make requests." This demand is further amplified by the emergence of advanced AI models that "reason" or take time to plan and deliver answers to complex queries. The evolution of reasoning AI models, such as DeepSeek R1, has led to a surge in computational requirements. Nvidia's CEO, Jensen Huang, highlighted that these next-generation models demand up to 100 times more computational resources.

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Major tech companies, including Microsoft and Meta, are investing heavily in infrastructure to meet these escalating demands. The Financial Times notes that

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"analysts predict a significant increase in investments for inference facilities, with expectations that inference will account for the majority of AI-related computational demand in the near future."

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Inference computing is effectively the real-time application of trained models. Agentic apps, that leverage inference, are gaining in popularity and power as software vendors work to simplify how people, teams, and companies work. By creating agents that can not only complete simple tasks but be predictive, personalized, and able to optimize their own workflows over time, the speed and efficiency of work increases dramatically and enables humans to focus on higher value priorities. All of this promises to contribute positively to the bottom line so it makes sense that businesses of all sizes, as well as consumers, are interested in agentic powered apps.

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All of this points to one thing - the need for specialized infrastructure to support the increasing adoption of AI will continue to grow. For the business leader this means finding a vendor who can not only support your needs today but who can also scale with you is critical.

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WhiteFiber is excited about the journey we are on with our current and future customers to support them as they deliver AI powered innovation. To that end, we are also excited to note that our initial fleet of B200s have arrived, are racked, and will be available soon. If you’re looking for a partner to help you adopt and scale, we’d be happy to talk with you.