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질문 1 의 8

What should a team define before comparing AI hardware?

모든 질문 미리보기8 질문

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  1. What should a team define before comparing AI hardware?
    • The workload, such as training, interactive inference, or on-device processing
    • 모델 매개변수만 계산됩니다.
    • A vendor’s peak operation count
    • The model name
  2. Why does peak arithmetic performance alone not predict application speed?
    • Peak figures include monitor refresh rate
    • Memory movement, software, and other bottlenecks can dominate
    • All workloads are compute-bound
    • Arithmetic is unrelated to models
  3. What should be checked alongside compute capacity?
    • Only the published weight-file size
    • Only the arithmetic throughput at maximum utilization
    • Memory capacity and bandwidth, including buffers and cached state
    • Only the sum of memory across all devices
  4. What does the guide caution about memory spread across multiple devices?
    • It always becomes one seamless pool
    • It eliminates communication costs
    • It guarantees model quality
    • Aggregate memory is not automatically one simple pool
  5. What should a team evaluate when using lower-precision numerical formats?
    • Accuracy changes on the intended model and task
    • Only whether model files became smaller
    • Only whether the numerical format is advertised
    • The peak operation rate without running the model
  6. When is an advertised hardware capability actually used?
    • Whenever a chip has a high peak score
    • When hardware support, kernels, and the framework use it
    • Only when a benchmark says so
    • When listed in a brochure
  7. Which measurements does the guide recommend for reproducible hardware comparisons?
    • Only peak arithmetic
    • Only a vendor demo
    • Latency, throughput, power, and cost per useful task with stated settings
    • Processor count alone
  8. One billion parameters stored at 16 bits each takes about how much weight storage in the worked example?
    • 200 MB for the whole system
    • 20 GB including all runtime buffers
    • 2 GB including activations and cache
    • 2 GB for weights alone; other memory needs are excluded