Rehber bağlantılı sınav · Orta Seviye

Yapay Zeka Sınavı için GPU ve TPU

Donanımı seçmeden önce GPU ve TPU mimarilerini, yazılım desteğini, sistem kısıtlamalarını ve ekiplerin ihtiyaç duyduğu iş yüküne özgü kanıtları karşılaştırın.

İlgili kılavuz yollarıGPU vs Yapay Zeka İçin TPU
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Which frameworks does the guide list as supported for Cloud TPU workloads?

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  1. 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
  2. TPU'yu kim tasarlıyor?
    • NVIDIA
    • Google
    • Intel
    • Apple
  3. Hangi çekirdek donanım yapısı TPU'ları matris çarpımında verimli kılar?
    • Dönen bir manyetik disk
    • Büyük bir mekanik aktarma bankası
    • Çoklu-birikimli birimlerin sistolik dizisi
    • Tek bir ultra hızlı çekirdek
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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