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Ad tech models map content to the IAB Content Taxonomy and the audience signals that programmatic advertising runs on. ZeroGPU offers a base IAB classifier tuned for real-time categorization at the edge plus an enriched variant that adds topics, keywords, and inferred intent. Each request is shown for both the Responses API and the OpenAI-compatible Chat Completions API.

zlm-v1-iab-classify-edge

ZeroGPU’s IAB classifier maps any text to the industry-standard IAB Content Taxonomy in a single, fast inference call. Each call returns categories across both the 1.0 and 2.2 taxonomies plus matched audience segments, every result scored for confidence. With support for 50+ languages, it classifies multilingual content without a translation step. At 90M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge. When you need clean category and audience signals on every request, this is the model — and when you need the full profile, reach for the enriched variant.
References: Model docsTermsPrivacy
Returns IAB audience categories plus content matches across taxonomy versions, each with a confidence score.
Response

zlm-v2-iab-classify-edge-enriched

The enriched variant of ZeroGPU’s IAB classifier turns a single inference call into a full content-intelligence profile not just a label, but everything a contextual pipeline needs to act on. Each call returns IAB categories across both the 1.0 and 2.2 taxonomies (down to tier-3), audience and interest segments, topics, keywords, and user-intent classification — all with confidence scores with edge-native, sub-millisecond speeds. With support for 50+ languages, it delivers the same full profile on multilingual content without a translation step. Built for contextual ad targeting, brand-safety scoring, publisher content categorization, and signal pipelines that need full metadata on every request.
References: Model docsTermsPrivacy
In addition to scored audience and content categories, the enriched model returns taxonomy codes and parent IDs, plus topics, keywords, and an inferred user_intent.
Response

zlm-v1-iab-domain-classifier

ZeroGPU’s Domain IAB Classifier maps a raw domain name straight to the IAB Content Taxonomy in a single, fast inference call, returning content categories, topics, keywords, and user-intent signals. Because it needs only the domain as input, it reduces payload size by up to 10x compared to page-level classification workflows while still surfacing the contextual signals that bidstream enrichment, contextual targeting, and domain-level intelligence pipelines run on. At 149M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge.
References: Model docsTermsPrivacy

Domain-level classification

Page-level classifiers like zlm-v1-iab-classify-edge and zlm-v2-iab-classify-edge-enriched take the full body of an article or page as input and map that content to the taxonomy. zlm-v1-iab-domain-classifier works one level up: it takes only the raw domain name (for example nytimes.com) and infers the categories, topics, keywords, and intent that characterize the site as a whole. That shift has two practical effects:
  • Up to 10x smaller payloads. You send a single hostname instead of crawling and shipping page text, which makes it well suited to bidstream enrichment, where the domain is often the only contextual signal available and per-request size matters.
  • No page fetch required. Use it when you have the domain but not the rendered content — domain-level intelligence pipelines, allow/deny list scoring, and contextual targeting at the inventory level. When you do have the page text and need per-URL precision, reach for the page-level classifiers instead.
Returns scored content categories across taxonomy versions plus topics, keywords, and an inferred user_intent for the domain.
Response