GPU Inference
GPU inference is the process of using a trained machine learning model to make predictions or generate outputs on new, unseen data. While training requires massive computational power to adjust model weights, inference is the operational phase where the finalized model is deployed to perform tasks in a real-world application.
In modern AI applications, the speed and efficiency of inference directly impact user experience and operational cost. Low-latency inference is critical for real-time systems like autonomous vehicles, live recommendation engines, and chatbots. Efficient GPU utilization ensures that high-throughput AI services can scale affordably.
When a model is trained, its parameters are fixed. During inference, the input data (e.g., an image, a text prompt) is fed through the model's architecture. The GPU, with its thousands of parallel processing cores, excels at performing the massive matrix multiplications required by neural networks simultaneously. This parallel processing capability is what allows complex models to execute predictions in milliseconds.
