Two phases of the AI lifecycle
AI training builds or updates models using large datasets and sustained heavy compute. AI inference runs trained models to produce outputs for users and applications. Both depend on GPU compute infrastructure but with different workload patterns.
Training workloads
Training repeatedly adjusts model weights across epochs. It tends to need high VRAM, long runtimes, and cluster-scale compute for large models. Data-center power and cooling are essential.
Inference workloads
Inference serves predictions, text, images, or classifications in production. It often prioritizes latency, throughput, and cost per query. Inference demand can grow as AI products reach more users, though patterns vary by market.
Frequently Asked Questions
What is AI training?
Training is the process of teaching a model using large datasets and intensive GPU compute over extended periods.
What is AI inference?
Inference is running a trained model to generate predictions, answers, images, or other outputs for users or applications.
Do training and inference need the same hardware?
Both use GPUs, but training often needs more memory and sustained compute, while inference may prioritize latency and throughput.