Models, artifacts, and runtimes · Lesson 9 of 16 · 5 min

Compare quantization profiles

Quantization changes representation and memory use, and it can affect task quality. Measure both resource behavior and held-out quality for the intended workload.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-models-runtimes-009.json. Separate the recorded facts from your assumptions.
Hint & worked example
Quantization changes representation and memory use, and it can affect task quality. Measure both resource behavior and held-out quality for the intended workload.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-models-runtimes-009.json
```
Common mistake to avoid: Lower memory use automatically means a better release.
2
Isolate the deciding signal
Select the q4_quality_pass field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Hold this q4 candidate despite its lower memory use because it failed the quality gate.
A successful JSON query isolates q4_quality_pass.
```
jq .q4_quality_pass /opt/ai-lab/cases/aiops-models-runtimes-009.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: hold-q4, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-models-runtimes-009-decision.txt.
Hint & worked example
Hold this q4 candidate despite its lower memory use because it failed the quality gate.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'hold-q4' > /home/learner/lab/aiops-models-runtimes-009-decision.txt
```
Common mistake to avoid: Lower memory use automatically means a better release.

Understand the pattern

Quantization changes representation and memory use, and it can affect task quality. Measure both resource behavior and held-out quality for the intended workload.

Interpret the evidence

Hold this q4 candidate despite its lower memory use because it failed the quality gate.

A tempting mistake

Lower memory use automatically means a better release.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-models-runtimes-009.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "q8_memory_gib": 8,
  "q4_memory_gib": 4.5,
  "q4_quality_pass": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .q4_quality_pass /opt/ai-lab/cases/aiops-models-runtimes-009.json

false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'hold-q4' > /home/learner/lab/aiops-models-runtimes-009-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “hold-q4” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Compare quantization profiles” justify?Lower memory use automatically means a better release.Make the change now and gather the missing evidence afterward.Hold this q4 candidate despite its lower memory use because it failed the quality gate.

Apply it elsewhere

A colleague concludes: “Lower memory use automatically means a better release.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Read memory requirements Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision