Low Barrier to Entry
Minimum 2-node deployment. Full platform capabilities from day one. No need to build a full GPU cluster before experimenting.
ZStack AIOS is an independently developed AI infrastructure platform built around three integrated layers — compute power, model services, and operations — giving enterprises a complete private AI platform without stitching together multiple tools.
Don't settle for public cloud APIs. Keep your AI infrastructure where it belongs — under your control.
Public cloud AI APIs require sending your proprietary data — customer records, financial models, internal documents — to third-party servers. Private AI keeps sensitive data under your governance, always.
Cloud GPU pricing compounds fast. At enterprise inference volumes, the economics of on-premises GPU infrastructure become significantly more favorable. You own the hardware. You control the cost.
Fine-tuning proprietary models on public cloud platforms means your training data and model weights live on someone else's infrastructure. Private deployment means full ownership of every training run and every model artifact.
Regulated industries — financial services, healthcare, government — cannot use public AI APIs without extensive legal review. Private AI eliminates the compliance question entirely.
Every capability from GPU scheduling to AI application deployment — built in, not bolted on.
Deploy AI workloads on bare metal, VMs, or containers — with 1% GPU granularity, 95% passthrough performance, and unified heterogeneous scheduling across your entire GPU fleet.
Full lifecycle model services — training, evaluation, inference, and RAG — managed through one platform with intelligent task decomposition and distributed parallel training.
Cross-platform metering, multi-tenant isolation, and elastic fault tolerance — enterprise-grade governance and security for production AI workloads at any scale.
Every design decision optimized for production private AI — not adapted from general-purpose infrastructure.
Minimum 2-node deployment. Full platform capabilities from day one. No need to build a full GPU cluster before experimenting.
Data management → model training → inference → app deployment. One platform, one interface, no integration projects.
Dynamic GPU partitioning maximizes hardware utilization. The same GPU cluster serves more teams, more workloads, with less waste.
95% GPU passthrough performance for training. High-performance storage network optimized for AI I/O patterns. Adaptive load balancing for inference.
Localized data management. File-level isolation. HA and DR built in. Your models, your data, your infrastructure — entirely under your control.
ZStack AIOS supports heterogeneous GPU environments, eliminating the need to standardize on a single vendor before running enterprise AI.
Fine-tune foundation models on proprietary datasets across industries including media, healthcare, education, government, and telecommunications. ZStack AIOS provides everything from compute scheduling to industry-specific training dataset storage — a complete end-to-end training environment on your own infrastructure.
Run inference workloads for production AI applications using on-premises GPU resources. Dynamic scheduling ensures inference SLAs are met even as demand fluctuates, while keeping all data on your own infrastructure.
Deploy RAG knowledge base applications locally. Support multiple inference service orchestration strategies and plugin integrations. Quickly deploy AI applications — chatbots, document analysis, vision systems — without sending data to external APIs.
No rip-and-replace. ZStack AIOS runs on top of the GPUs you already own and the ZStack platforms you already run, with support for mainstream Chinese and international AI accelerators.
Real deployments across finance, healthcare, and government — where data sovereignty is non-negotiable.
"The university adopted the ZStack Cloud platform to enable flexible scheduling of GPU resources in both GPU passthrough and vGPU modes. This significantly improved resource utilization and reduced total cost of ownership (TCO). At the same time, fine-grained tenant isolation enhanced security and resource efficiency."
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"The company deployed ZStack Cloud on its two A100 GPU servers, virtualizing physical GPUs into multiple independent computing units, providing unified scheduling and multi-tenant isolation, so a single server can serve multiple tenants' AI training workloads."
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"Amid accelerating global smart-city development, a large Middle Eastern city upgraded its public safety video surveillance system (CCTV) to an intelligent platform by deploying the ZStack Cloud platform. This platform, with elastic computing and GPU integration, enabled real-time video analytics at scale."
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Talk to an engineer or download the brief — whichever fits your timeline.