The Speed Detective

For a platform lead whose model responds slower than it should.

One signal · 8 specialists · one result

Does the work1
Keeps knowledge1
Runs infrastructure4
Audits and guards2

How the work moves

Six steps, each done by one specialist

Starts with

A model that responds slower than it should

First

1Profiles the engine at system, chip and memory levelGPU Profiler 2Traces hardware events and ties each delay to a causeHardware Event Tracer 3Breaks each response down into where the time wentAI Speed Investigator

Then

4Records the conditions of every run for fair comparisonRun Conditions Recorder 5Tests each proposed fix as an experiment against a baselineAI Experiment Runner 6Checks that the answers stayed the sameAI Answer Accuracy Checker

Ends with

A clear account of where time goes and tested fixes

the signalthe resultEach step is done by one specialist on the crew

The story

The agent profiles the engine, traces hardware events and breaks each response down into where the time went. It records the conditions of every run so results can be compared fairly. Each proposed fix is tested as an experiment, with a check that the answers stayed the same.

The crew

The specialists behind this role, grouped by the kind of work they do.

Runs infrastructure

AI Speed InvestigatorThis agent finds where the time goes in each AI response, from the full request down to waiting, copying and the slowest steps, and compares serving modes side by side.Also serves The Model Launch Manager
GPU ProfilerThis agent profiles the AI engine at system, chip and memory level, finds idle time under steady load and records GPU use.Also serves The Engine Builder
Serving Capacity TunerSets how much GPU memory holds conversations in progress and when new requests wait, then measures how many run at once.Also serves The Model Launch Manager
Hardware Event TracerThis agent records low-level hardware events and queue activity and attributes each delay to its cause.

Does the work

AI Experiment RunnerThis agent runs each experiment the same way: it sets up a baseline, packages the experiment, runs it on the GPUs with measurement attached and compares the result with the baseline.Also serves The Research Lab

Keeps knowledge

Run Conditions RecorderThis agent records GPU memory and machine load around every run so results are read against the conditions they ran in.Also serves The Archive Keeper

Audits and guards

AI Answer Accuracy CheckerThis agent checks whether a change alters the model's answers, finds where any difference starts and gives a clear yes or no.Also serves The Model Launch Manager, The Model Shrinker
AI Service Load TesterThis agent runs the model under realistic traffic against a standard setup, checks each memory-saving path and compares runs.Also serves The Model Launch Manager

Where this crew also works

The Model Launch Manager44

Each bar counts the specialists this role shares with another.

The engineering behind this role

High-throughput batch inference and embeddingsCapability Measure the instrumentIdea The observer that changed the answerEngineering story The thirteen percent that was never missingEngineering story From 44K to 163K tokens per secondEngineering story All capabilitiesThe engineering, by what it does