SkillVaultskills Browse all 1,000+ skills

AI Engineering · Version 1.3.0 · Reviewed 2026-08-02

Model Serving Capacity Planner

Make AI behavior measurable and safer for GPU capacity modeling and KV-cache sizing with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Models prefill and decode demand, KV-cache memory, batching, concurrency, token distributions, GPU replicas, and latency headroom.

₹199 one-time

Get this skill archive

Install in your AI coding tool

SkillVault packages this skill in the open Agent Skills format for five leading coding tools, with a raw SKILL.md fallback for every other compatible IDE or agent.

See the complete graphical installation and usage guide

What this skill helps you do

  • GPU capacity modeling
  • KV-cache sizing
  • Batching policy

How Model Serving Capacity Planner works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for GPU capacity modeling

It decides

A KV-cache sizing change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Model Serving Capacity Planner models prefill and decode demand, KV-cache memory, batching, concurrency, token distributions, GPU replicas, and latency headroom. Use it when the work involves GPU capacity modeling, KV-cache sizing, Batching policy.

  1. Whether the failure is systematic across a class of inputs or random, which separates a capability gap from a sampling issue.
  2. Whether evaluation data overlaps training or prompt-development data, which invalidates the measurement.
  3. Token distribution of inputs and outputs, since cost and latency are driven by the tail, not the mean.
  4. Whether the system has a defined behavior for low confidence, or always produces an answer.
  5. Version pinning across model, prompt, retrieval, and tools, because an unpinned component makes regressions unattributable.

Failure modes it recognizes

  • Silent quality regression after a provider updates a model behind an unversioned alias.
  • Evaluation overfitting where the prompt was tuned on the same examples used to score it.
  • Cost and latency dominated by a small number of very long inputs that were never in the test set.
  • Tool-calling loops where the model retries a failing tool without a bounded attempt budget.
  • Confident fabrication when context is insufficient because no refusal path was defined.
  • Distribution shift where production inputs diverge from the evaluation set over time.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a regression of a few percent.
  • Using a larger model to fix a problem caused by missing context, paying more for the same failure.
  • Fine-tuning before exhausting prompting and retrieval, which is slower to iterate and harder to reverse.
  • Using an LLM judge without validating the judge against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled evaluation set and a baseline before changing anything; without a baseline there is no improvement, only change.
  • Pin every version and change one component at a time.
  • Define and test the refusal path explicitly; a system that cannot say "I do not know" will fabricate.
  • Budget latency and cost on p95 token counts, not averages.

Evidence it asks for

  • Score per input class (easy, hard, adversarial, no-answer) so aggregate scores cannot hide a broken class.
  • Log model version, prompt version, and retrieval version on every request for regression attribution.
  • Track p50 and p95 tokens and cost per successful task, not per call.

The method inside

  1. Define the measured baseline and user-visible target for GPU capacity modeling.
  2. Attribute the dominant cost or latency mechanism affecting KV-cache sizing.
  3. Rank batching policy changes by expected impact, confidence, effort, and regression risk.
  4. Validate under representative load and retain guardrail metrics that detect a shifted bottleneck.

Deliverables

  • GPU capacity modeling assessment
  • KV-cache sizing decision and action plan
  • Batching policy verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

How many GPUs do we need for 40 requests per second with 3,000-token prompts and 300-token outputs?

Expected output

Separate prefill compute from decode bandwidth and model the real prompt/output distribution. KV cache sets concurrency before raw FLOPS; size replicas from p95 token counts and the target time-to-first-token...

Boundaries and compatibility

Ideal for

  • GPU capacity modeling: produce a decision or artifact grounded in supplied evidence.
  • KV-cache sizing: produce a decision or artifact grounded in supplied evidence.
  • Batching policy: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.