Understanding the moment when AI models transform from learning to real-world problem solving
Inference is when an AI model applies what it has learned to make predictions in real-time. It's the AI model's time to shine — its moment of truth, a test of how well the model can apply information learned during training to make a prediction or solve a task.
A student studies thousands of math problems to understand the underlying patterns and rules. The AI model learns from massive datasets to understand relationships and encode them into weights.
When faced with a new math problem on an exam, the student applies what they learned. Similarly, AI inference is when the model uses its training to solve new, unseen problems in real-time.
Let's see how AI inference works with email spam detection
The model is fed thousands of labeled emails:
The model learns patterns like excessive exclamation marks, suspicious keywords, unusual sender addresses, and encodes these into its neural network weights.
A new email arrives in real-time:
On average, 90% of an AI model's life is spent in inference mode, making predictions rather than training.
Popular AI services handle billions of inference requests daily, each requiring real-time processing.
Modern applications require inference responses in milliseconds for optimal user experience.
While training happens once, inference happens millions or billions of times. This is why optimized hardware specifically designed for inference workloads is crucial for cost-effective, energy-efficient AI deployment.
Our specialized ASIC-based servers are designed specifically for high-performance inference workloads.