Guide-linked quiz · Medium Level
GPU vs TPU for AI Quiz
Compare GPU and TPU architectures, software support, system constraints, and the workload-specific evidence teams need before choosing hardware.
Question 1 of 8
Which frameworks does the guide list as supported for Cloud TPU workloads?
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- Which frameworks does the guide list as supported for Cloud TPU workloads?
- JAX, TensorFlow, and PyTorch/XLA
- CUDA C++ only
- PyTorch CPU and ONNX Runtime only
- TensorFlow Lite only
- Who designs the TPU?
- NVIDIA
- Intel
- Apple
- What core hardware structure makes TPUs efficient at matrix multiplication?
- A spinning magnetic disk
- A large mechanical relay bank
- A systolic array of multiply-accumulate units
- A single ultra-fast core
- Which system factors can affect a GPU-versus-TPU performance comparison?
- Only the accelerator’s peak operation count
- Only the number of parameters in the model
- Only the cloud region name
- Model support, software, interconnects, memory, utilization, and deployment cost
- What is a practical strength of GPUs relative to TPUs for many teams?
- A broad ecosystem of CUDA libraries, tools, and existing GPU-supported workloads
- A guarantee that every model is faster on a GPU
- A requirement that all training use TensorFlow
- A built-in advantage in every cost comparison
- Why does the guide recommend benchmarking the intended workload on the specific platform?
- Peak operation counts always predict end-to-end speed exactly
- Measured results depend on workload shape, kernels, input pipeline, memory, and system scaling
- All workloads use the same hardware path and precision
- A benchmark removes the need to check software support
- What should a team compare before choosing between a GPU and a TPU for training?
- Peak FLOPS alone for an unrelated precision
- The chip’s name and product launch date
- End-to-end time and cost for its model, framework, input pipeline, and target scale
- A benchmark from a different model and software stack
- How does Google describe a TPU pod?
- A single GPU with no network
- A CPU-only cluster
- A model format
- A group of TPU chips connected through a high-speed interconnect
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