Abstract
Enterprise AI adoption has moved from "isolated experiments" into the "systematic deployment" stage. Inference has overtaken training to become the mainstream workload, production-grade stability and governance compliance have become baseline requirements, and enterprises urgently need a single platform that can manage heterogeneous compute, deploy model services, and govern AI applications. At the same time, "one cloud, multi-architecture" is becoming a standard requirement for enterprise AI infrastructure. Driven by domestic-technology policy and supply-chain security, enterprise IT environments now contain a mix of NVIDIA GPUs, Hygon DCUs, Cambricon MLUs, and other accelerators, and unified onboarding and scheduling of these devices has become a core pain point. MaaS (Model-as-a-Service) is reshaping how enterprise AI applications are delivered: with standardized model service interfaces, a unified access gateway, and a metering system, enterprises can embed AI capabilities into business processes as APIs, cutting delivery cycles from weeks to hours. The rise of AI Agents in turn imposes entirely new security and elasticity requirements on infrastructure: multi-agent collaboration is becoming the new paradigm, and agents that call model APIs at high frequency and execute complex tasks need deep support from secure container sandboxes, intelligent routing, and autoscaling. Taken together, an open, neutral, and reliable AI infrastructure platform is the best choice for enterprises: it avoids hardware and software lock-in, protects existing IT investment, and supports an independently controllable evolution path. These are also the core considerations when enterprises select an AI infrastructure platform.
EasyStack introduces EAF (EasyStack AI Foundation) — an enterprise-grade AI infrastructure platform built for private deployment scenarios, helping enterprises efficiently build and operate their own AI infrastructure. EAF adopts a three-in-one architecture of "compute governance + model services (MaaS) + application delivery", providing full-stack capabilities from unified onboarding of heterogeneous AI accelerators and full model lifecycle management to one-click delivery of AI applications. Core platform features include:
- One cloud, multi-architecture: unified management of NVIDIA GPUs, Hygon DCUs, and other mainstream domestic and international AI accelerators, meeting domestic-technology and supply-chain security requirements.
- Fine-grained partitioning and intelligent scheduling: supports MIG / HAMi partitioning and topology-aware scheduling, raising resource utilization to over 75%.
- MaaS platform: built-in inference engines including vLLM, SGLang, and Llama.cpp, an OpenAI-compatible API gateway, and from model to service in 10 minutes.
- AI application marketplace: built-in RAG knowledge base, Open WebUI, AI Agent, and other templates with one-click deployment, shortening the delivery cycle from weeks to 10 minutes.
- Enterprise-grade governance: multi-tenant isolation, RBAC permissions, audit logs (180 days), and data that never leaves the domain, meeting the high compliance requirements of finance, government, and other sectors.
- Agent-ready: secure container sandboxes, a unified model gateway, and autoscaling provide infrastructure support for multi-agent collaboration.
EAF supports starting from a single node and scales smoothly on ECF/ECNF, protecting existing investment. It has been deployed in finance, government, manufacturing, and other industries, significantly improving AI application launch efficiency and resource utilization. The platform is committed to being an open, neutral, and reliable enterprise AI foundation, making AI infrastructure easy for every enterprise to build and run.