Question 1

What is AI inference at the edge?

Accepted Answer

AI inference at the edge means running model inference close to where data is created, on edge devices and edge servers, instead of sending every request to a centralized GPU datacenter. Edge inference lowers latency and inference cost for high-volume workloads such as moderation, classification, extraction, reranking, and embeddings. ZeroGPU is an edge inference cloud that runs these workloads behind a single AI inference API.

Question 2

What are small language models?

Accepted Answer

Small language models (SLMs) are compact models, typically well under the size of frontier models, that are specialized for focused tasks. For narrow, repetitive work they can match or outperform larger general-purpose models while using far less compute. SLM inference on ZeroGPU is combined with open-weight inference so developers can pick the right model per workload.

Question 3

What is ZeroGPU?

Accepted Answer

ZeroGPU is the edge inference cloud. It runs inference on specialized small and open-weight models for the repeatable tasks inside AI applications. Instead of sending every request to a frontier model, teams send high-volume workloads to ZeroGPU, which runs them across edge devices and cloud infrastructure.

Question 4

Is ZeroGPU a replacement for LLMs?

Accepted Answer

No. ZeroGPU works alongside frontier models. Use frontier models for complex reasoning, and use ZeroGPU for repeatable workloads that can be handled by specialized models.

Question 5

Do I need to change my code to use ZeroGPU?

Accepted Answer

No. ZeroGPU exposes an OpenAI-compatible API. Point your existing client at our endpoint, set the model name, and eligible workloads run on ZeroGPU. Most teams integrate in under an hour.

Question 6

How does ZeroGPU reduce inference costs?

Accepted Answer

ZeroGPU runs inference on specialized small models at the edge, which are cheaper and faster than large frontier models for tasks like summarization, classification, signal extraction, PII detection, moderation, and lightweight decisioning.

Question 7

What types of workloads should run on ZeroGPU?

Accepted Answer

ZeroGPU is designed for repeatable production workloads including document analysis, summarization, classification, signal extraction, PII detection, moderation, and lightweight decisioning.

Question 8

What is the ZeroGPU Inference Cloud?

Accepted Answer

The ZeroGPU Inference Cloud combines specialized models, optimized runtimes, cloud infrastructure, and global edge capacity into one inference layer. Buyers use a simple API while ZeroGPU handles execution and reliability.

Question 9

How do developers integrate ZeroGPU?

Accepted Answer

ZeroGPU provides OpenAI-compatible APIs, so developers can send selected workloads to specialized models using familiar request formats.

ZeroGPU sells to AI agents - no signup

AI inference at the edge

Small models. Big performance.

USE THE RIGHT MODEL FOR THE JOB

Lower cost. Lower latency. By design

ZLMs

Open-weight & SLMs

Built with the AI ecosystem

Drop into your stack with an OpenAI-compatible API

The right compute for every AI workload

Edge capacity, available globally

Specialized models, everywhere

Edge capacity backed by cloud

Frequently asked questions

Run your next AI workload on ZeroGPU