Analysts predict a shift in market focus to inference equipment by 2026

By 2026, the AI hardware industry will shift its primary development focus from training models to running them, as the growth in computational loads driven by the widespread adoption of LLM forces tech giants to rethink their strategies for purchasing and developing specialized chips. According to forecasts, the key driver of the market will be the optimization of latency and power consumption during inference, with companies expected to actively invest in specialized architectures, such as Cerebras, which offer an alternative to traditional GPU for working with huge volumes of parameters in real-time. Among the main trends is the transition to custom solutions that allow reducing the cost of one token, with industry experts pointing to the need to integrate hardware accelerators directly into the data center infrastructure, which will reduce scaling costs by 2-3 times compared to current levels based on standard graphics accelerators. Changes in the hardware stack will also affect memory management methods and data bus bandwidth, with the transition to specialized chips for inference allowing companies to run models with 70B+ parameters on a smaller number of nodes, which is critical for maintaining the profitability of AI products in conditions of growing demand for low latency response. This shift marks the end of the era of universal GPU dominance in model deployment tasks, with companies that first implement specialized hardware gaining a significant advantage in terms of user maintenance costs and query processing speed.