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GPU vs TPU untuk Kuis AI

Bandingkan arsitektur GPU dan TPU, dukungan perangkat lunak, batasan sistem, dan bukti spesifik beban kerja yang dibutuhkan tim sebelum memilih perangkat keras.

Jalur panduan terkaitGPU Vs TPU Untuk AI
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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. Siapa yang mendesain TPU?
    • NVIDIA
    • Google
    • Intel
    • Apple
  3. Struktur perangkat keras inti apa yang membuat TPU efisien dalam perkalian matriks?
    • Disk magnetik yang berputar
    • Bank relai mekanis yang besar
    • Susunan sistolik yang terdiri dari unit-unit yang terakumulasi berkali-kali
    • Satu inti ultra-cepat
  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