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ZeroGPU is the compute efficiency layer for AI inference. It runs repeatable, high-volume AI tasks on specialized small and nano language models across an edge-powered network - so the same workloads run faster and cheaper than on centralized GPUs. Send a request to one OpenAI-compatible endpoint; ZeroGPU picks the right small or nano model and runs it on the right compute. Frontier models for reasoning. ZeroGPU for repeatable execution - classification, extraction, moderation, summarization, routing - at the scale AI demands.

Make your first call

Find your API key and project ID in the ZeroGPU dashboard.
Already using the OpenAI SDK? Point it at ZeroGPU:
🎉 Either call returns the same classification result:

What you can run

Text classification

Text generation

PII detection

Summarization

What you get

Lower cost and latency

Specialized small and nano models on distributed compute. ~10x faster and 50%+ cheaper on production tasks.

One OpenAI-compatible API

POST /v1/responses; integrate with minimal code changes.

Built for production

Token usage, latency, and volume per request, with project isolation for dev, staging, and production.

How it works

1

Create a project

One organization, multiple projects. Each gets its own API key and dashboard.
2

Send requests

POST to /v1/responses with your key, project ID, and model. Response comes back as structured JSON.
3

Monitor everything

Token usage, request volume, latency, error rates: all visible in the dashboard. Debug individual requests in Logs.
Workload analysis → Model selection → Edge orchestration. ZeroGPU classifies each task, picks the fastest viable model, and routes it to the right compute. See How ZeroGPU works for the full picture.

Go deeper

Quickstart

How ZeroGPU works

Model Catalog

API Reference