A recent Forbes article estimated that by 2026, nearly two-thirds of the processing power associated with AI will be directed toward AI inference, not AI training, and that more companies will invest in AI-optimized servers and ultra-low-latency networks. The trend is also reflected in the Uptime Institute Annual Global Data Center Survey 2025, which shows that almost 30% of data center operators are already running AI training or inference workloads, as well as in IDC forecasts projecting more than 1.3 billion active AI agents by 2028.
Public discussions often focus on AI training, yet the stage that actually makes models work in the real world—AI inference—frequently remains in the background. In an ecosystem where AI is increasingly embedded into business processes, critical applications, and user experiences, inference is the component that delivers tangible value, while training remains the foundation.
AI Training vs. AI Inference
If training is the process through which a model learns from large datasets, inference is the moment when the model applies what it has learned. During the training stage, the algorithm builds its internal logic, adjusts parameters, and refines itself through successive iterations. It is then optimized through fine-tuning and moved into production.
In the AI inference stage, the already-trained model receives new data—images, text, signals, transactions—and generates predictions, classifications, recommendations, or decisions. In this phase, the AI no longer analyzes historical information; instead, it interprets new, previously unseen situations in real time.
How AI Inference Works in Practice
The inference process has several key stages: the model must be deployed in a production environment, new data must be processed and transformed into the correct format, and then the model performs the actual prediction using the patterns learned during training. The result is interpreted and translated into an action—an alert, a recommendation, a classification, or an automated decision.
A clear example is medical applications that analyze X-rays. The patient’s image is prepared in the format used during training, the model searches for anomalies—such as fractures or tumors—and within milliseconds it generates a probability score for each potential diagnosis. The physician receives the conclusion and uses it in the decision-making process. It’s a workflow in which AI provides real-time expert support.
The same principle appears in finance (instant fraud detection), retail (experience personalization), logistics (route optimization), industry (equipment monitoring), cybersecurity (traffic anomaly detection), or media (automated content analysis). Any application that uses AI in production relies on an inference process.
AI Inference Models
There are several types of AI inference, each adapted to the needs of specific applications. Batch inference is used when data can be processed in large batches at regular intervals—for example, in financial analyses or aggregated reporting. Online or real-time inference responds within milliseconds and is essential for applications such as chatbots, shopping assistants, or autonomous vehicles.
Streaming inference allows models to process continuous data streams, such as those generated by industrial sensors or IoT devices, keeping processes stable and anticipating issues.
Finally, edge inference moves the model directly onto the devices that generate the data—from smart cameras to drones or industrial sensors—reducing latency and significantly improving data protection.
What Infrastructure Does an AI Inference Project Need
An AI inference project requires an IT infrastructure optimized for a balance of processing power, latency, and cost. Although AI inference is generally less expensive than training, the challenge lies in the need to deliver fast and consistent responses and to provide results to users quickly. This requires high-performance compute resources, including GPUs optimized for inference, multi-core CPUs for data processing, and, in some cases, accelerators such as TPUs or FPGAs. Memory plays a critical role: large models require servers with extended memory, high-speed NVMe storage, and optimized interconnects such as PCIe or NVLink.
The network is another essential element: real-time applications require extremely low latency—typically under one millisecond—which demands modern data center architectures, multi-path connectivity, and intelligent routing. In addition, a mature inference project needs advanced observability and monitoring capabilities, automatic scaling, Kubernetes orchestration, and more. Last but not least, security and data protection are critical, especially in sensitive sectors such as healthcare, finance, or the public sector.
From Concept to Real Value, with M247 Global
M247 Global provides a complete IT infrastructure that enables organizations to run AI inference projects at global scale within private cloud environments. From next-generation servers to high-performance data center colocation services and high-speed connectivity, M247 Global offers all the components needed to run complex AI applications.
More details are available on the dedicated page: Deploy and Run Large Language Models (LLMs) on Your Own Network
As AI becomes an integral part of modern business architecture, inference is the mechanism through which models transform information into decisions, actions, and measurable outcomes. From conversational assistants and fraud detection to autonomous vehicles and advanced analytics systems, nearly every AI application in production today relies on a fast, accurate, and optimized inference process.
Organizations that invest in modern AI inference infrastructure gain a real advantage: the ability to turn data into applied value. However, this is possible only with a solid data architecture, well-trained models, and high-performance compute and network resources—key elements that separate true innovators from the rest of the market.