Distributed AI inference for geo-aware edge compute.
Run production workloads with lower cost and lower latency — horizontal scale across edge devices, one familiar API surface.

• Lower cost — Inference on idle edge compute instead of always renting centralized GPU capacity.
• Geo-aware routing — Requests land on nearby capacity so latency stays predictable for real users.
• Edge-native models — Nano language models (NLMs) tuned for edge and cloud, not only downsized cloud stacks.
• OpenAI-compatible API — POST /v1/responses and POST /v1/chat/completions with request shapes you already know.
• Cloud fallback — When edge is unavailable, the same API path falls back to cloud without a second integration story.
• Typed SDKs — Official clients on npm (zerogpu-api) and PyPI (pip install zerogpu-api → import zerogpu), plus Go, Ruby, Java, Rust, C#, PHP, and Swift in the SDK monorepo.

curl https://api.zerogpu.ai/v1/responses \
  -H "x-api-key: $ZEROGPU_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b-instruct-fast",
    "input": "Summarize: NASA announced that its Artemis III mission is now scheduled for late 2026 ..."
  }'

Set ZEROGPU_API_KEY the same way you do in the platform dashboard snippets. Full authentication, models, and error semantics live in docs.zerogpu.ai.