Memory and device evidence

Audio Buffer Memory Budget for Offline AI

Bound recording, segmentation, decoding, transcript, and enhancement buffers.

Last reviewed: 2026-09-16 · Fact IDs: COVE-ASR-01, MEMORY-01

Direct answer

Storage, load success, representative-task peak, sustained peak, and device support are different claims and require different evidence.

This page does not publish benchmark or compatibility results. It shows the evidence required to answer offline audio buffer memory without turning an assumption into a product claim.

Evidence to collect

The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:

  • Audio format contract
  • Segment duration policy
  • Sustained recording peak

A device badge must come from an exact device, OS, artifact, runtime, backend, and workload record, not parameter-count intuition.

Implementation workflow

  1. Record the exact artifact and format.
  2. Measure the app before model load.
  3. Measure after model initialization.
  4. Run a representative product task.
  5. Repeat the fixed workload without restarting.
  6. Store failures, fallback, and cleanup behavior.

Keep each transition observable. A failure should identify the stage, artifact, runtime, and recovery action without logging private user content.

Failure patterns to prevent

  • Holding an entire session in memory
  • Mixing raw audio and model memory

Also prevent silent fallback, unpinned artifacts, missing cancellation, and conclusions that combine unlike configurations. Store unsuccessful runs alongside successful ones.

Minimum reproducibility record

LayerRecord
DeviceManufacturer, model, chipset, RAM class, operating-system build
SoftwareApplication version and git commit
ModelFamily, variant, revision, format, file length, hash, quantization
RuntimeName, revision, requested backend, observed backend evidence
WorkloadFixture revision, input hash, prompt hash, output policy
OutcomeCompleted, failed, cancelled, fallback, and privacy-safe diagnostics

Release checklist

  • ☐ The primary query is answered without an unsupported number.
  • ☐ Every artifact and runtime is pinned.
  • ☐ The representative task and failure policy are explicit.
  • ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
  • ☐ User-content and network boundaries are documented.
  • ☐ Result wording applies only to the recorded configuration.
  • ☐ The page links to raw method or evidence when results are added.

Sources and related evidence

This page relies on the Cove fact ledger and benchmark policy. Results remain blocked until raw records exist.

Chinese deployment and troubleshooting content is organized in the 奇连 AI 端侧专题.