ZeroGPU × Dappier: real-time classification at scale
A purpose-built ZLM edge model replaced a general-purpose model + RAG pipeline, keeping accuracy while cutting latency and cost across Dappier's live publisher network.
Dappier brings AI answers and a new ad surface
Dappier distributes a fully-trained "Ask AI" stack to media publishers, who embed it on their sites. In return, Dappier holds exclusive monetization rights to that conversational surface, serving native ads backed by direct relationships with major blue-chip advertisers.
Before ZeroGPU: slow and expensive
A general-purpose nano model (GPT-5.4-nano) paired with a Pinecone RAG lookup handled IAB classification and intent-signal extraction.
Model inference dominated per req ~1,800–2,000 ms; the RAG lookup added ~100 ms more (query + network)
- RAG added infrastructure complexity and per-query cost
- ~2,000 ms latency was too high for real-time ad surfaces
- Token spend scaled linearly with traffic
After ZeroGPU: right-sized ZLM models on the edge
Two purpose-built ZLM models, trained on IAB Content Taxonomy 2.2 + Audience Taxonomy 1.1, fine-tuned and proprietary to ZeroGPU. The RAG lookup was eliminated entirely.
| Metric | Before | After | Δ |
|---|---|---|---|
| Input tokens | 2,000–3,000 | 400 | ~6× fewer |
| Output tokens | 700 | 500 | lower |
| Latency | ~1,800–2,000 ms | 48 ms p50 | ~10× faster |
| Cost / 1M req | $1,375 | $216 | ~6× cheaper |
| Accuracy | baseline | maintained | ✓ |
Model tokens only; RAG infrastructure also eliminated.
Not every task needs a frontier model.
We've been using ZeroGPU's ZLM models at Dappier for IAB classification and signal extraction across our AI interactions, and it's been a meaningful upgrade. We've seen roughly 10x faster latency and significant cost savings compared to using frontier models for the same tasks.