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Models, artifacts, and runtimes · Lesson 10 of 16 · 6 min

Read memory requirements

Weight size is not the total serving memory requirement. Leave space for the KV cache, runtime buffers and concurrent work before declaring an artifact a fit.

Open the labCourse map
Mastery 0/3Run itExplain itApply it
learner@clairosSimulated AI Ops lab
learner@clairos:~ $
↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Understand the pattern

Weight size is not the total serving memory requirement. Leave space for the KV cache, runtime buffers and concurrent work before declaring an artifact a fit.

Interpret the evidence

Reduce the 17 GiB planned demand or use more available memory before loading.

A tempting mistake

A 10 GiB weights file fits every 16 GiB serving workload.

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-010.json
{
  "fixture": "Fictional training data; not a live measurement.",
  "weights_gib": 10,
  "cache_gib": 4,
  "buffers_gib": 3,
  "available_gib": 16
}

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

$ jq .available_gib /opt/ai-lab/cases/aiops-models-runtimes-010.json
16

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

$ printf '%s\n' 'reduce-memory-demand' > /home/learner/lab/aiops-models-runtimes-010-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 “reduce-memory-demand” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read memory requirements” justify?

Apply it elsewhere

A colleague concludes: “A 10 GiB weights file fits every 16 GiB serving workload.” What should you do?

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Inspect accelerator availabilityTry an incident drill

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Keep learning

  1. 01Compare quantization profilesQuantization changes representation and memory use, and it can affect task quality. Measure both resource behavior and held-out quality for the intended workload.Practice
  2. 02Inspect accelerator availabilityAccelerator availability has several layers: device visibility, driver/runtime support and free memory. A visible GPU does not prove that the model runtime can use it.Practice
  3. 03Spot a runtime mismatchMismatch diagnosis starts with explicit compatibility evidence. When a runtime rejects an architecture, changing prompt text cannot repair the loader contract.Practice

Case methodology: evidence, limits, decision