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Local Models, Performance and Cost Control

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Lesson 1.1 · 40 minutes

Classify the workload before choosing a model

Turn a vague request for a cheaper or local model into a testable workload brief with quality, privacy, latency, and ownership constraints.

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Local Models, Performance and Cost Control

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Verifiedon 2026.7.1

Action boundary

Before you act

Expected result
A one-page workload brief names the user outcome, data class, quality bar, latency ceiling, budget boundary, and owner.
Failure mode
A model is chosen from a benchmark chart or anecdote before the workload and non-negotiables are defined.
Rollback
Keep the existing route unchanged and mark the candidate decision as unproven.

The decision is not “local or hosted”

That question is too blunt to operate. Start with a unit of work: a support reply, a classification pass, a research brief, a tool-calling sequence, or a long document transformation. A model route is only useful when it serves that unit of work under named constraints.

Build a workload card

Field Write this, not a slogan
Job The user-visible result and what counts as useful
Input Data class, typical and maximum size, allowed retention
Output Format, correctness bar, and who can approve it
Service Target latency, volume, concurrency, and availability window
Budget Per-run ceiling, monthly guardrail, and owner
Risk What an incorrect, delayed, or disclosed result would cost

Learner artifact — decision boundary map: Draw the workload in the centre. Put hard constraints (data handling, geographic or network boundary, maximum delay, required tool use) in a red ring. Put preferences (style, vendor familiarity, novelty) in an outer grey ring. Label the person who may trade off each hard constraint.

Lab: turn a request into an experiment

Choose one non-sensitive task. Write five cases that represent normal, difficult, long-context, malformed-input, and refusal/escalation conditions. Replace names, identifiers, and customer content with safe fixtures. For each case, write a pass condition that another reviewer could judge without guessing.

Do not let a pass condition become “looks good.” Better: “returns a three-field JSON record, cites only supplied evidence, and flags missing evidence.”

Checkpoint

Which statement belongs in a workload brief?

  • A. “Use the smartest model available.”
  • B. “Keep median response time pleasant.”
  • C. “For the five fixture cases, return the required fields; no unsupported claims; median end-to-end time stays below the team’s agreed ceiling.”

Answer: C. It gives a result, a quality rule, a measurement population, and an owned threshold. A and B are preferences disguised as requirements.

Evidence note

This lesson is original commissioning material, not a product-configuration guide. It deliberately makes no claim about a particular OpenClaw provider key, local runtime, or routing setting. Record those separately against current primary documentation before publication.

Source provenanceVerification and sources

Review receipt rr_local_models_workload_policy_fixture

Outcome
approved
Method
source-review
Reviewer
academy-specification-review
Reviewed

Evidence

  • academy-spec — curriculum-local-models-contract; snapshot 1c60fa76ac90…

Limitations

  • Approval covers the original workload, benchmark, routing, quota, and fallback method for 2026.7.1; it makes no universal provider, model, GPU, performance, privacy, or savings claim.

Open the public evidence snapshot

Lesson checkpoint

Ready to move on?

Mark this lesson complete when you can apply its outcome without relying on the examples above.