Developers connect to ZeroGPU through an OpenAI-compatible API and select the open-weight, small, or nano language model that best fits their workload. Requests are routed to optimized inference infrastructure, allowing companies to run AI tasks with lower latency and significantly lower cost. ZeroGPU supports popular models such as DeepSeek, GLM, and Kimi, along with specialized models for classification, moderation, signal extraction, enrichment, and other high-volume tasks. Customers pay only for the tokens they use, with no infrastructure to manage.
Run production AI workloads at significantly lower cost with transparent, pay-as-you-go token pricing.
Access leading open-weight models, including DeepSeek, GLM, Kimi, and more, without managing your own infrastructure.
Run production AI workloads at significantly lower cost with transparent, pay-as-you-go token pricing.
Use specialized small and nano models for high-volume, repeatable tasks such as classification, moderation, enrichment, and signal extraction.
Integrate quickly using familiar OpenAI-compatible endpoints, making it easy to migrate existing applications.
Use specialized small and nano models for high-volume, repeatable tasks such as classification, moderation, enrichment, and signal extraction.
Access leading open-weight models, including DeepSeek, GLM, Kimi, and more, without managing your own infrastructure.
Process real-time and batch workloads with infrastructure designed for low latency and high-volume inference.
Process real-time and batch workloads with infrastructure designed for low latency and high-volume inference.
Deploy models across managed cloud, private infrastructure, or edge environments based on your performance, privacy, and cost requirements.
Integrate quickly using familiar OpenAI-compatible endpoints, making it easy to migrate existing applications.
Deploy models across managed cloud, private infrastructure, or edge environments based on your performance, privacy, and cost requirements.