AI inference at the edge.
ZeroGPU runs high-volume AI workloads on specialized small and open-weight models, for lower cost and lower latency at production scale.
Performance varies by workload, model, and configuration.
Small models. Big performance.
Most AI workloads don't need a frontier model. Specialized small models can match or beat larger general-purpose models on focused tasks, with lower latency and more efficient inference.
Purpose-built ZeroGPU Language Models
ZLMs are trained for specific high-volume production tasks: content classification, intent and signal extraction, content moderation, and structured decisions for agents and workflows.
Open-weight models, serverless
Leading open-weight small and nano models — Qwen, DeepSeek, Llama, GLM, GPT-OSS and more — hosted on the same inference cloud. No provisioning, no idle cost, usage-based pricing per token.
Drops into your stack
An OpenAI-compatible API means teams switch selected workloads with a base-URL change, with usage, latency and cost visibility per request and per model.
The right compute for every workload
Inference is routed across edge devices, edge servers and cloud capacity. Small, frequent tasks run efficiently at the edge; larger models fall back to the cloud for reliability.