Over the past decade, model training has been the driving force behind artificial intelligence: millions of parameters, compute clusters, and big data have defined an era. However, the focus of innovation and adoption is shifting to another key moment in the AI lifecycle: inference.
This is the stage when trained models go into action, make decisions in real time, and have an impact on applications as diverse as medical diagnostics, fraud detection, content recommendation, conversational assistance, and more. Inference is the bridge between the promise of AI and its concrete value to users, organizations, and governments. This is where prediction turns into action, and the true return on investment in artificial intelligence materializes.
“Inference is the protagonist in the mass adoption of Artificial Intelligence: trained models begin to impact the real experience of users,” says Klaas van der Kolk, Business Development Executive at AMD.
This shift also involves a new set of challenges: reducing latency, improving power efficiency, and scaling performance without relying on proprietary or expensive infrastructure. Faced with this scenario, AMD proposes a powerful, open and complete alternative: an ecosystem where processors, accelerators, software and open-source work together to maximize the value of inference. This architecture allows customers to run models such as Llama 3, Kimi K2.5, or DeepSeek-R1 with fewer nodes, low power consumption, and fast output generation speeds.
But inference isn’t just about GPUs. Datacenter processors optimized for inference play a crucial role in this ecosystem too. Thanks to their ability to handle AI workloads with high computational density and energy efficiency, they allow inference to be implemented in a scalable way, both in traditional data centers and in hybrid or edge architectures. This combination of powerful CPUs and specialized GPUs enable scenarios where prediction occurs in real time, close to the user, and without compromising performance.
In markets where resources may be more limited and infrastructure more diverse, having efficient and open platforms is key. Inference makes it possible to realistically bring AI to sectors such as healthcare, education, agriculture, and financial services, without requiring large initial deployments. And it does so with a pragmatic view: accurate prediction, low response times and controlled costs. This approach also empowers local developers and innovators, who can now build solutions with accessible tools and open standards.
“What defines the future of AI is not only how advanced the technology is, but how accessible and efficient its implementation can be,” adds van der Kolk.
If training was the first stage of this revolution, inference is the point where AI becomes an everyday tool. Companies like AMD aren’t just recognizing this transition—they are driving it forward with solutions designed to scale, adapt, and respond to today’s demands.
“With a proposition that combines performance, efficiency, and openness, at AMD we are helping to define the future of AI. Because when AI stops being a promise and becomes an everyday tool, the ecosystem is everything”, adds van der Kolk
This is a sponsored post from AMD. The editorial staff is not responsible for the content.