On this page: Pain points · Decision matrix · Evaluation flow · Resource ceilings · Remote cost checklist · FAQ
Pair this matrix with telemetry field naming from the OpenTelemetry GenAI observability guide, align tokenizer and batch assumptions with the MLX-LM versus Transformers acceptance notes, and reuse dataset hygiene patterns from the local RAG chunk and embedding matrix when your eval mixes retrieval with generation.
Pain points teams hit on Mac-class LLM stacks
1. Optimizer overfit. Small dev slices and manual edits let DSPy compilation look brilliant until a held-out offline suite collapses.
2. Hardware theater. One-shot laptop runs mix GUI contention, thermal throttling, and ad hoc batch sizes, so throughput numbers are not comparable across days.
3. Economics blind spots. API list prices ignore hourly rent, idle GPU minutes, and rework from constraint violations, so finance cannot defend the rollout.
Decision matrix: artifact, role, and gate
| Artifact | Role | Typical gate |
|---|---|---|
| DSPy signature + teleprompter | Structured prompt program with typed inputs and outputs. | Schema-valid JSON rate above 99% on the offline suite before merge. |
| Compilation or bootstrap run | Teacher-assisted prompt search with bounded rounds. | Optimizer rounds capped; no teacher call without budget id in traces. |
| Held-out JSONL eval | Versioned benchmark with manifest hash. | Primary score within ±1.5% of last green baseline unless product approves. |
| Constraint suite | Policy checks for PII, toxicity, or tool misuse. | Zero tolerance items stay at 0; soft limits breach below 0.5%. |
| Remote soak report | Repeated eval on a dedicated Mac node. | p95 end-to-end latency under gate; rent plus tokens reconciled. |
In weekly program reviews, walk the table from top to bottom: confirm signatures are frozen, verify the manifest hash matches CI, then read constraint and latency gates before anyone argues about model creativity. If a gate fails, file a single remediation ticket that cites the metric, the slice, and the estimated dollar impact so product and finance can decide together.
Executable evaluation flow
1. Freeze manifests: publish SHA256, row counts, and license fields next to every offline split.
2. Encode signatures: keep prompts and few-shot selectors in code, not slack threads.
3. Run compile or bootstrap with pinned seeds and a maximum optimizer budget aligned to finance.
4. Execute the offline harness locally on Metal with the same quantization and context window you intend to ship.
5. Emit a single JSON report with aggregates, per-slice breakdowns, and worst offenders for debugging.
6. Replay the identical job on a rented remote Mac for at least four continuous hours, capturing power-stable p95 latency and idle cost.
Archive every run with the same semantic version string you tag in git so auditors can diff prompts, datasets, and hardware profiles without opening notebooks.
Resource ceilings for Apple Silicon baselines
Use these as starter guardrails for M4-class unified memory hosts; tighten after you profile your model width.
- Resident model footprint: keep at least 18% unified memory free for OS, eval harness, and tokenizer caches.
- Batch concurrency: cap simultaneous eval workers so aggregate prefill tokens stay below 75% of your steady-state ceiling.
- Thermal: pause optimizer runs if GPU average sustained above 92% for ten minutes without airflow clearance.
- Disk: reserve 60 GB on fast SSD for weights, adapter caches, and report archives per major experiment branch.
# Example environment knobs (illustrative; store secrets outside git)
EVAL_SEED=20260420
OFFLINE_EVAL_MANIFEST_SHA256=${OFFLINE_EVAL_MANIFEST_SHA256}
DSPY_MAX_TEACHER_ROUNDS=${DSPY_MAX_TEACHER_ROUNDS}
DSPY_MAX_BOOTSTRAP_DEMOS=${DSPY_MAX_BOOTSTRAP_DEMOS}
QUALITY_REGRESSION_MAX_DELTA=${QUALITY_REGRESSION_MAX_DELTA}
CONSTRAINT_HARD_FAIL_RATE_MAX=${CONSTRAINT_HARD_FAIL_RATE_MAX}
P95_LATENCY_MS_MAX=${P95_LATENCY_MS_MAX}
REMOTE_SOAK_MIN_HOURS=${REMOTE_SOAK_MIN_HOURS}
REMOTE_NODE_HOURLY_USD=${REMOTE_NODE_HOURLY_USD}Remote node cost acceptance checklist
- Hourly rent, projected soak duration, and idle minutes are listed beside API token spend.
- The remote host mirrors chip generation, memory size, and macOS major version from production rehearsal plans.
- Network egress for artifact upload is estimated and capped with alerts.
- Breaker or retry policies match the offline harness; no silent extra teacher calls.
- Final packet bundles eval JSON, manifest hash, and signed approval for any metric relaxation.
FAQ
Should DSPy optimization run on every pull request? Keep heavy compilation off the critical path. Run fast regression evals on frozen prompts per PR and schedule optimizer jobs nightly or manually.
Why is remote soak mandatory if local numbers look fine? Laptops sleep, share CPUs with IDEs, and vary fan curves. Dedicated remote nodes mirror how you host long jobs and stabilize tail latency.
What if quality rises but latency breaches the gate? Block release or negotiate a new gate with product and finance; never silently widen latency budgets in the same release train.
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