MEASUREMENT · LOCAL AIFramework defined · representative runs pending

Performance Lab

Is local execution actually good enough for the workload and hardware that matter? Performance Lab turns that question into a reproducible measurement decision.

LatencyThroughputMemoryThermal / battery

See the method ↓

Can it run?

Fit the model and runtime on the target hardware.

Can it run well?

Measure responsiveness, throughput and resource pressure.

Should it run locally?

Translate the measurements into an architecture decision.

METHOD

Preserve the run context before collecting the number.

Performance only becomes decision evidence when the workload, model, runtime, device and configuration are explicit enough to reproduce and interpret the result.

Workload

Start from the real task

The benchmark only matters if it represents the interaction, context size and sustained behavior the product actually needs.

Model

Record the exact candidate

Model family, parameter scale and quantization remain part of the evidence rather than disappearing behind one score.

Runtime

Keep the execution layer explicit

Runtime and backend choices can materially change latency, throughput and resource behavior.

Device

Measure where the decision lives

Representative hardware is mandatory when memory, thermals or battery determine whether local execution is viable.

Configuration

Make the run reproducible

Context, concurrency and other relevant settings travel with the result so comparisons remain interpretable.

EVIDENCE SURFACE

Measure the constraints that change product viability.

Latency, generation speed, memory and sustained device behavior are the core dimensions. This page deliberately does not display invented benchmark values while representative runs are still missing.

TTFTResponsiveness

How long until useful generation begins?

DecodeGeneration speed

Can output arrive fast enough for the target experience?

MemoryResource fit

Does the workload fit without destabilizing the device?

Thermal / BatterySustainability

Can the performance survive sustained use?

EVIDENCE BOUNDARY

The measurement framework exists; the representative benchmark dataset does not yet.

Performance Lab defines the decision questions, run context and core dimensions required to compare Local AI systems. Until real runs are published with model, runtime, configuration and hardware context, numeric or chart-based performance claims remain intentionally absent.

Supported today

Decision questions are explicit: fit, practicality and deployment boundary
Latency, throughput, memory and thermal/battery are defined as core dimensions
Run context is expected to include workload, model, runtime, configuration and hardware
Results are designed to feed back into Local vs Hybrid vs Cloud decisions

Not claimed here

Published representative benchmark runs
Reproducible model/runtime/device comparison table
Validated workload-specific thresholds for good-enough performance

INTERPRETATION

The finding is workload suitability, not the raw score.

A tokens-per-second value has no architectural meaning by itself. The result becomes useful only after it is interpreted against the experience, device and operational constraints of the real workload.

MEASUREObserved run

Latency · throughput · memory · sustained device behavior

INTERPRETGood enough for this workload?

Use the product requirement as the threshold, not a generic leaderboard.

DECIDELocal · Hybrid · Cloud

Change the execution boundary when the measured trade-off requires it.

STATUS & ARCHITECTURAL CONSEQUENCE

No benchmark claim until the representative runs exist.

The next useful increment is evidence, not more presentation: attach real runs with enough context that another reader can understand what changed and why it matters.

CURRENT DECISION

Measurement is allowed to reject Local AI.

Performance Lab exists to prevent local-first from becoming ideology. A workload should stay local only when representative evidence shows that quality, responsiveness and resource use remain acceptable for the actual product context.

Keep Local when the workload fits the target hardware and experience
Use Hybrid when only part of the workflow is practical locally
Keep Cloud when measured constraints make local execution the wrong trade-off

CONNECTED SYSTEM

Where this project fits

BUILD · INFRASTRUCTURELocal LLM ServerA runtime candidate to measure

MEASURE · EVIDENCEPerformance LabTranslate runtime behavior into viability evidence

DECIDE AGAIN · OUTCOMELocal · Hybrid · CloudThe architecture can change after measurement