Qc Ai provides a self‑hosted platform for deploying large AI models using its proprietary Chitu inference engine, offering high‑throughput inference on on‑premise, appliance or private‑cloud infrastructure. The solution keeps data local, reduces compute costs, and includes end‑to‑end tools for model management, scaling, and ongoing support, targeting enterprises and developers needing secure, cost‑effective AI deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers often face high costs, complex integration, and data security concerns when deploying large AI models on public cloud services or using generic open‑source inference tools.
Solution
Qc Ai offers a self‑hosted, end‑to‑end large‑model deployment platform that combines its proprietary “赤兔” (Chitu) inference engine with customizable software and hardware bundles. The solution enables private deployment on on‑premise servers, dedicated appliances, or a private cloud, ensuring that sensitive data remains local while meeting specific business workflows. By optimizing inference throughput, the Chitu engine reduces compute expenses and delivers faster response times compared with typical open‑source stacks. Qc Ai also provides full lifecycle support—including installation, upgrades, and ongoing operations—to streamline deployment and minimize the engineering effort required to bring large models into production.
Target Audience
Primary customers are enterprises, system integrators, and AI developers who need secure, cost‑effective large‑model inference capabilities on their own infrastructure.
Features
- Proprietary Chitu inference engine with top‑ranking throughput performance in AIPerf benchmarks
- Flexible deployment options: software‑only for existing hardware, integrated appliance bundles, or private‑cloud instances
- End‑to‑end management tools for model deployment, versioning, scaling, and automated maintenance
- Local data storage and processing to satisfy strict security and compliance requirements
- Business‑level customization allowing model fine‑tuning and integration with enterprise workflows
- Comprehensive support for hardware selection, installation, and performance optimization