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

Model Serving Capacity Planner

Name: Model Serving Capacity Planner
Brand: SkillVault
Price: 99 INR
Availability: InStock

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.

₹99 one-time

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What this skill helps you do

GPU capacity modeling
KV-cache sizing
Batching policy

How Model Serving Capacity Planner works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for GPU capacity modeling

It decides

A KV-cache sizing change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

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.

Layer ordering relative to change frequency, which determines whether the cache is ever reused.
Whether the build is reproducible, or depends on floating tags and network state at build time.
Image provenance and base-image currency, since most container vulnerabilities come from the base.
Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
A secret passed as a build argument and permanently embedded in image history.
A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
Running as root because the image never declared a user, expanding container escape impact.
A cache key that includes a timestamp, so the cache never hits.
Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
Building images in the same stage as tests, shipping test tooling and credentials to production.
Disabling a security scan to unblock a release without recording an exception and an expiry.
Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

Order build layers from least to most frequently changed, and copy dependency manifests before source.
Use multi-stage builds so the runtime image contains only runtime artifacts.
Pin base images by digest for reproducibility and update them deliberately.
Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
Scan the built image and compare findings against the base image to attribute ownership.
Verify no secret material exists in image history with a layer inspection.

The method inside

Define the measured baseline and user-visible target for GPU capacity modeling.
Attribute the dominant cost or latency mechanism affecting KV-cache sizing.
Rank batching policy changes by expected impact, confidence, effort, and regression risk.
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 custom agents
Claude Agent Skills / SKILL.md
Any instruction-following chat model

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.