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<title data-rh="true">ML model benchmark | neokapi</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" property="og:image" content="https://neokapi.github.io/img/og-card.png"><meta data-rh="true" name="twitter:image" content="https://neokapi.github.io/img/og-card.png"><meta data-rh="true" property="og:url" content="https://neokapi.github.io/ml-benchmark"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" property="og:locale:alternate" content="nb"><meta data-rh="true" name="docusaurus_locale" content="en"><meta data-rh="true" name="docusaurus_tag" content="default"><meta data-rh="true" name="docsearch:language" content="en"><meta data-rh="true" name="docsearch:docusaurus_tag" content="default"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" name="twitter:site" content="@neokapi"><meta data-rh="true" property="og:title" content="ML model benchmark | neokapi"><meta data-rh="true" name="description" content="What it costs to run kapi's small-model content checkers in-process: download size, load time, inference latency, and memory — and what that means for running checks standalone versus server-side."><meta data-rh="true" property="og:description" content="What it costs to run kapi's small-model content checkers in-process: download size, load time, inference latency, and memory — and what that means for running checks standalone versus server-side."><link data-rh="true" rel="icon" href="/img/favicon.png"><link data-rh="true" rel="canonical" href="https://neokapi.github.io/ml-benchmark"><link data-rh="true" rel="alternate" href="https://neokapi.github.io/ml-benchmark" hreflang="en"><link data-rh="true" rel="alternate" href="https://neokapi.github.io/nb/ml-benchmark" hreflang="nb"><link data-rh="true" rel="alternate" href="https://neokapi.github.io/ml-benchmark" hreflang="x-default"><link rel="stylesheet" href="/assets/css/styles.a74d5c23.css">
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(voice/style similarity, register, do-not-translate by entity) are served by small, open, multilingual models run in-process through the same ONNX runtime the segmenter uses. The cost a user pays is not the per-sentence inference — that is cheap — but the <strong>model download</strong> and the<!-- --> <strong>resident memory</strong>. This page measures both, so the choice between running a check on your machine and running it server-side is grounded in numbers.</p><h2>Measured cost per model</h2><div style="overflow-x:auto"><table style="width:100%;border-collapse:collapse;font-size:0.92rem"><thead><tr><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Model / variant</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">What it checks</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Download</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Load</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Inference</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Peak memory</th><th style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">License</th></tr></thead><tbody><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>e5-small</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Voice / style similarity (sentence embeddings)</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">464.8<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">248.1 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">4.7 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">1183.3<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">MIT</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>e5-small-O4</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Voice / style similarity — fp16-optimized (O4) variant</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span>240.5<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">183.2 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">4.68 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">520<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">MIT</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>e5-small-int8</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Voice / style similarity — int8-quantized variant</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span>129.2<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">83.1 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">2.66 ms</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span>39.5<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">MIT</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>gliner-multi</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Do-not-translate / entity spotting (zero-shot NER)</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">1119.1<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Apache-2.0</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>gliner-multi-int8</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">GLiNER — int8-quantized variant (download/footprint mitigation)</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">348.5<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">Apache-2.0</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>formality</code> <span style="background:rgba(244,114,114,0.12);color:#d65a5a;border-radius:6px;padding:1px 8px;font-size:0.8rem;font-weight:600">no ONNX yet</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Register / formality classification</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span>2.8<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">academic (s-nlp)</td></tr><tr><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums"><code>sat-3l-sm</code></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:left;font-variant-numeric:tabular-nums">Reference: the segmenter kapi already ships (kapi-sat)</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums"><span style="color:#d65a5a">408.5<!-- --> MB</span></td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">—</td><td style="padding:8px 12px;border-bottom:1px solid var(--ifm-table-border-color);text-align:right;font-variant-numeric:tabular-nums">MIT</td></tr></tbody></table></div><p style="font-size:0.85rem;color:var(--ifm-color-emphasis-600)">Darwin arm64<!-- --> · Python <!-- -->3.11.6<!-- --> · onnxruntime <!-- -->1.26.0<!-- -->. Inference is the mean over repeated runs on short multilingual sentences. <!-- -->onnxruntime CPU; the Go plugin bundles libonnxruntime (~18 MB) per platform<!-- -->. Size-only rows are not yet wired for in-process inference (GLiNER's zero-shot input differs; the formality ranker ships PyTorch weights that need an ONNX export). “Peak memory” is the resident set the loaded session adds.</p><h2>What the numbers say</h2><ul><li><strong>Per-sentence inference is not the cost.</strong> A loaded embedding model scores a sentence in single-digit milliseconds — fast enough to run on every block in a pipeline.</li><li><strong>The full-precision footprint is the cost.</strong> The fp32 export of a 118M-parameter embedding model is a ~465 MB download and over a gigabyte of resident memory — too heavy to load casually inside a CLI that runs next to your editor and your build.</li><li><strong>Quantization changes the verdict.</strong> The int8 export of the same model is a ~129 MB download and ~40 MB resident — and slightly faster. That is small enough to ship as an explicitly-installed plugin and cache, which makes a single small-model checker viable to run on your machine.</li><li><strong>Some models stay heavy even quantized.</strong> The generalist NER model is ~1.1 GB at full precision and still ~330 MB int8 — defensible as an opt-in download, but a poor default for a laptop, and a natural fit for a server that hosts it once.</li></ul><h2>Standalone, or server-side?</h2><p>The deterministic checks — terminology, do-not-translate by string, placeholder and tag integrity, register by lexicon — have no model and no download; they always run locally and free. The question is only where the <em>model-backed</em> checks run. Three options:</p><h3>Option A — small model local, heavy models server-side (recommended)</h3><p>Ship the int8 embedding model as an optional plugin the user explicitly installs (~129 MB, ~40 MB resident) for voice/style similarity and register; run the generalist NER and any LLM-deep check server-side, where the model is hosted once and amortized across a team and across large batches. Keeps the CLI lean and offline-capable for the common case, without asking every user to download a gigabyte.</p><h3>Option B — all model-backed checks server-side</h3><p>kapi stays purely deterministic offline; every ML-backed check is a call to a server you run. Simplest CLI and smallest install, at the cost of the offline subjective checks and a network dependency for them.</p><h3>Option C — all models local (quantized)</h3><p>Ship int8 exports of every checker (embedding ~129 MB + NER ~330 MB + register). Maximal offline capability and no server needed, at the cost of a few hundred MB of one-time downloads and a heavier resident footprint when several run together.</p><p>The data points to <strong>Option A</strong>: int8 makes one small model cheap enough to live in the CLI, while the heavy generalist model earns its keep server-side — which is also where batch volume (tens of thousands of strings across many languages) is most economical to process.</p><h2>How the model is acquired</h2><p>A checker that needs a model should acquire it <strong>explicitly</strong>, never by a surprise download in the middle of a <code>kapi check</code>. Consumer ML tools (Hugging Face <code>transformers</code>, Whisper) lazy-download on first use, which is convenient but hangs the first run and fails in airgapped or CI environments. Developer tools make it explicit and pinnable — <code>vale sync</code>, <code>spacy download</code>,<code>ollama pull</code> — and kapi already follows that model for its native deps (<code>kapi plugin install sat</code>). The model-backed checker is the same: an opt-in plugin you install (its model bundled in the release tarball, the way the segmenter bundles the ONNX runtime, or pulled by an explicit step), so the download is a deliberate, cacheable, offline-after-install action with a known version.</p><p>When the plugin or its model is absent, <code>kapi check</code> still runs every deterministic check and reports the model-backed check as unavailable with the one command that enables it — fail-closed with guidance, not a silent network call. In CI, the install is a setup step (as connector and plugin installs already are), so runs stay deterministic and offline once the cache is warm.</p><p>This is realized today as the <code>kapi-check</code> plugin (<code>kapi plugins install check</code>, then <code>kapi-check pull</code> downloads the int8 model) and <code>kapi check --voice</code>, which scores each block against a brand profile's examples and reports an advisory finding below the <code>--voice-min</code>cosine cutoff. Because multilingual embedding cosines cluster high, that cutoff is calibrated per profile rather than shipped as a universal number — the honest stance for a proxy.</p></main></div><footer class="theme-layout-footer footer footer--dark"><div class="container container-fluid"><div class="row footer__links"><div class="theme-layout-footer-column col footer__col"><div class="footer__title">Documentation</div><ul class="footer__items clean-list"><li class="footer__item"><a class="footer__link-item" href="/kapi/get-started/quickstart">Get started</a></li><li class="footer__item"><a class="footer__link-item" href="/kapi/overview">Kapi</a></li><li class="footer__item"><a class="footer__link-item" href="/framework/architecture">Framework</a></li><li class="footer__item"><a class="footer__link-item" href="/kapi/cli">Kapi CLI</a></li><li class="footer__item"><a class="footer__link-item" href="/toolbox/overview">CLI tools</a></li><li class="footer__item"><a class="footer__link-item" href="/react/introduction">Kapi React</a></li><li class="footer__item"><a class="footer__link-item" href="/reference">Reference</a></li><li class="footer__item"><a class="footer__link-item" href="/formats">Format Reference</a></li></ul></div><div class="theme-layout-footer-column col footer__col"><div class="footer__title">More</div><ul class="footer__items clean-list"><li class="footer__item"><a href="https://github.com/neokapi/neokapi" target="_blank" rel="noopener noreferrer" class="footer__link-item">GitHub<svg width="13.5" height="13.5" aria-label="(opens in new tab)" class="iconExternalLink_oqBP"><use href="#theme-svg-external-link"></use></svg></a></li><li class="footer__item"><a href="https://github.com/neokapi/homebrew-tap" target="_blank" rel="noopener noreferrer" class="footer__link-item">Homebrew Tap<svg width="13.5" height="13.5" aria-label="(opens in new tab)" class="iconExternalLink_oqBP"><use href="#theme-svg-external-link"></use></svg></a></li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright">Copyright © 2026 neokapi contributors. 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