Latency Benchmarks: TTFT, Tokens per Second, and User Feel

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-local-latency-benchmarks-creators

1. What is the core idea behind "Latency Benchmarks: TTFT, Tokens per Second, and User Feel"?

   1. A local model that is technically capable can still feel bad if time-to-first-token or generation speed is too slow.
   2. A local AI stack can include small safety models that classify prompts or output…
   3. llama.cpp
   4. reasoning budget

2. Which term best describes a foundational idea in "Latency Benchmarks: TTFT, Tokens per Second, and User Feel"?

   1. tokens per second
   2. TTFT
   3. throughput
   4. streaming

3. A learner studying Latency Benchmarks: TTFT, Tokens per Second, and User Feel would need to understand which concept?

   1. TTFT
   2. throughput
   3. tokens per second
   4. streaming

4. Which of these is directly relevant to Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. TTFT
   2. tokens per second
   3. streaming
   4. throughput

5. Which of the following is a key point about Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. Define the user task in one sentence.
   2. Choose the smallest model and runtime that might pass that task.
   3. Run one happy-path prompt and one failure-path prompt.
   4. Record speed, memory pressure, output quality, and the exact reason for any failure.

6. Which of these does NOT belong in a discussion of Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. Choose the smallest model and runtime that might pass that task.
   2. A local AI stack can include small safety models that classify prompts or output…
   3. Run one happy-path prompt and one failure-path prompt.
   4. Define the user task in one sentence.

7. What is the key insight about "Fresh check" in the context of Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. A local AI stack can include small safety models that classify prompts or output…
   2. llama.cpp
   3. Local runtime docs and Apple Silicon studies measure time-to-first-token, throughput, prompt processing, context length,…
   4. reasoning budget

8. What is the key insight about "Common mistake" in the context of Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. A local AI stack can include small safety models that classify prompts or output…
   2. llama.cpp
   3. reasoning budget
   4. Reporting only tokens per second and ignoring time-to-first-token, prompt length, streaming, and perceived responsivenes…

9. What is the recommended tip about "Benchmark before committing" in the context of Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

   1. Run your actual task samples against candidate models before choosing.
   2. A local AI stack can include small safety models that classify prompts or output…
   3. llama.cpp
   4. reasoning budget

10. Which statement accurately describes an aspect of Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

    1. A local AI stack can include small safety models that classify prompts or output…
    2. A local model that is technically capable can still feel bad if time-to-first-token or generation speed is too slow.
    3. llama.cpp
    4. reasoning budget

11. What does working with Latency Benchmarks: TTFT, Tokens per Second, and User Feel typically involve?

    1. A local AI stack can include small safety models that classify prompts or output…
    2. llama.cpp
    3. Benchmark three local models with short, medium, and long prompts, then translate the numbers into user experience notes.
    4. reasoning budget

12. Which of the following is true about Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

    1. A local AI stack can include small safety models that classify prompts or output…
    2. llama.cpp
    3. reasoning budget
    4. The big idea: measure the feel. A local model app is not done when the model answers once; it is done when the whole workflow can be install…

13. Which best describes the scope of "Latency Benchmarks: TTFT, Tokens per Second, and User Feel"?

    1. It focuses on A local model that is technically capable can still feel bad if time-to-first-token or generation sp
    2. It is unrelated to model-families workflows
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

14. Which section heading best belongs in a lesson about Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

    1. A local AI stack can include small safety models that classify prompts or output…
    2. Current source signal
    3. llama.cpp
    4. reasoning budget

15. Which section heading best belongs in a lesson about Latency Benchmarks: TTFT, Tokens per Second, and User Feel?

    1. A local AI stack can include small safety models that classify prompts or output…
    2. llama.cpp
    3. Build the small version
    4. reasoning budget