📦 NPM: https://www.npmjs.com/package/@qvac/sdk/v/0.19.1

QVAC SDK 0.19.1 is a patch on the 0.19 line. Completion stats now report prompt-processing throughput, deleteCache({ auto: true }) reclaims automatic KV caches without touching named ones, and assessModelFit can refuse a model from a computed floor when no calibration applies. Mobile withQvacSDK bundles no longer pull in the desktop-only fit subprocess, and @qvac/rag ^0.8.1 is the floor so Windows TurboVec installs the fixed package.

@qvac/sdk, @qvac/inference, and tetherto-qvac-sdk all ship at 0.19.1. Install @qvac/sdk and @qvac/inference together at this version.

Table of Contents

• New APIs
  • Prompt-processing throughput
  • Reclaim automatic KV caches
  • Computed floor when no calibration applies
• Features
• Bug Fixes

New APIs

Prompt-processing throughput

CompletionStats.promptTokensPerSecond is the addon's prefill (prompt-processing) rate. tokensPerSecond remains decode throughput. Both fields are optional; they appear on completionStats events and on the aggregated final.stats for single and batch completions. The Python client models include the same field.

const run = completion({ modelId, history, stream: true })
const stats = await run.stats

stats?.tokensPerSecond // decode throughput
stats?.promptTokensPerSecond // prompt-processing (prefill) throughput

Reclaim automatic KV caches

deleteCache({ auto: true }) drops every automatic cache that no in-flight turn is holding. Named, caller-owned caches are left alone. { all: true } still deletes everything, including named caches.

import { deleteCache } from '@qvac/sdk'

await deleteCache({ auto: true })

Computed floor when no calibration applies

On platforms without a calibration fixture, assessModelFit used to return unknown even when the artifact plus KV cache already exceeded the budget. It now falls back to a computed floor (artifact bytes plus the llama.cpp KV cache at the narrowest default width). Over budget is likely-too-large; otherwise the verdict stays unknown. This path never returns likely-fits.

Android compares the floor to the system-memory budget. iOS compares it to the process-memory budget, which now uses the per-process allowance from bare-os. Discrete GPUs without coefficients stay unknown.

New result fields: evidence (calibration | computed-only) and floorBytes.

import { assessModelFit, QWEN3_8B_INST_Q4_K_M } from '@qvac/sdk'

const result = await assessModelFit({
  models: [
    {
      model: QWEN3_8B_INST_Q4_K_M,
      workload: { kind: 'llm', contextTokens: 8192 }
    }
  ]
})

result.verdict // "likely-fits" | "likely-too-large" | "unknown"
result.evidence // "calibration" | "computed-only"
result.floorBytes

if (result.evidence === 'computed-only' && result.verdict === 'unknown') {
  // uncalibrated, not a near-miss
}

The assess-model-fit doc now includes a platform support matrix and a table of every unknown reason.

Features

iOS sample.memory.processAvailableBytes is sourced from bare-os availableMemory() so the process-memory budget can form. Other platforms leave that metric unavailable.

Bug Fixes

withQvacSDK mobile bundles defer bare-runtime/spawn and @qvac/model-fit/process, so expo prebuild no longer fails looking for a bare-posix android-arm64 prebuild that does not exist. Desktop advisory fit is unchanged.

@qvac/inference now requires @qvac/rag ^0.8.1. 0.8.0 could not open a TurboVec workspace on Windows.