What is a GPU? Why graphics processors also run AI computation
Explain GPU parallel processing, distinguish compute and memory from model speed and define task-validation conditions.
Understand the concept
A GPU performs highly parallel computation and is used beyond rendering in machine learning. It is not a model, and owning one does not make every model runnable. Workload, memory and software matter. Detection, loading and correct task completion require separate checks.
Parallel hardware needs compatible workloads and software
CUDA exposes GPU parallel programs and PyTorch supports device computation. Parallel advantages do not accelerate every application equally; preparation and serial work can dominate. Devices differ in operations and precision and backends have compatibility limits. Verify target operations rather than predicting complete service speed from core counts or advertised compute.
Check loading and full requests on the same model
For classification, record model, input length and actual device and check unintended CPU execution. Test a known short example before long inputs and concurrency. Observe peak resources, not download size. Separate memory failures from backend incompatibility. No device was inspected or benchmarked here and no buying rank or universal speed multiplier is offered.
Compute and memory cannot be reduced to one figure
Memory stores weights and runtime data while compute describes processing capability. Fast devices may not fit a model and sufficient capacity does not guarantee latency. Maintain drivers and frameworks and replay tasks after updates. Include sustained load and recovery. Select around compatibility and actual workloads rather than treating GPUs as a universal AI solution.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.