Maswali yanayounganishwa na mwongozo · Ngumu Kiwango

Maswali ya Kuboresha Maelekezo ya AI

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Swali 1 ya 8

Why measure end-to-end performance before optimizing a model?

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  1. Why measure end-to-end performance before optimizing a model?
    • Every workload is model-compute bound
    • Optimization always improves task quality
    • A short warm benchmark represents production use
    • The bottleneck may be retrieval, tokenization, transfer, or output handling rather than inference
  2. What can batching improve, and what can it worsen?
    • Throughput may improve while individual latency rises as requests wait for a batch
    • Both always improve together
    • Memory always decreases
    • Task accuracy is guaranteed to increase
  3. When is a cached model response safe to reuse?
    • Whenever the request text looks similar
    • When the key accounts for relevant input, model version, permissions, and context
    • When the cache is stored privately
    • Only when the answer is short
  4. What should be checked after reducing numerical precision?
    • Only whether inference became faster
    • Only the model’s file size
    • Task quality on the same representative evaluation set as the original configuration
    • Whether the GPU supports the format
  5. A benchmark uses one warmed-up short request. Why may it mislead?
    • It contains too many user cases
    • A warm request is never measurable
    • It evaluates the wrong model weights by definition
    • It omits cold starts, concurrency, long requests, and cancellation behavior
  6. What does time to first token measure compared with total completion time?
    • The initial streaming delay versus the time to finish the response
    • Two names for exactly the same metric
    • Model accuracy versus calibration
    • Queue depth versus cache hit rate
  7. Why tune one meaningful factor at a time?
    • It guarantees no regression
    • It helps attribute improvements and regressions to the change made
    • It removes the need for a baseline
    • It makes different workloads comparable automatically
  8. What is the optimization objective in the guide?
    • The highest isolated throughput number
    • The lowest latency on a single test
    • A better completed task within the required quality and resource constraints
    • The fewest model parameters regardless of quality