GPU Deployment
Also called: GPU-Based Deployment
The practice of deploying AI models or applications on graphics processing units (GPUs) to accelerate inference or training by leveraging massively parallel computation for high-performance workloads.
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Related terms
A Graphics Processing Unit (GPU) is a specialized parallel processor designed to perform large-scale mathematical computations efficiently, making it the primary hardware for training and running AI models.
Model ServingThe process of making an AI model available for inference by deploying it behind an interface that accepts requests and returns predictions or generated outputs.
Inference ServerA software service that hosts one or more AI models and processes inference requests by receiving inputs, executing model predictions, and returning outputs through APIs while managing resources, batching, scaling, and hardware acceleration.
Distributed InferenceAn inference approach that distributes model execution across multiple machines or accelerators to improve throughput, reduce latency, or support models that exceed the resources of a single device.
AutoscalingThe automatic adjustment of computing resources in response to changing workloads to optimize performance, availability, and cost efficiency.