Private AI Infrastructure Platform

Run Enterprise AI
On Your Infrastructure.

Under Your Control.

ZStack AIOS is a self-developed AI infrastructure operating system built around three integrated layers — compute power, model services, and operations — giving enterprises a complete private AI platform without stitching together multiple tools.

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1%GPU granularity

Precision scheduling down to
1% of GPU capacity

95%performance

Physical GPU performance
retained with passthrough

2nodes min

Start small, scale to a full
AI compute cluster

PrivateAI

Your data never leaves
your environment

Why Private AI

Why enterprises need
Private AI infrastructure

Don't settle for public cloud APIs. Keep your AI infrastructure
where it belongs — under your control.

Data never leaves your building

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.

Predictable cost at scale

Cloud GPU pricing compounds fast. At enterprise inference volumes, the economics of on-premise GPU infrastructure become significantly more favorable. You own the hardware. You control the cost.

Model customization without compromise

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.

Compliance by design

Regulated industries — financial services, healthcare, government — cannot use public AI APIs without extensive legal review. Private AI eliminates the compliance question entirely.

Architecture

One platform. Three layers.

Every capability from GPU scheduling to AI application deployment — built in, not bolted on.

Fully Integrated Stack

All three layers ship as one product — no integration projects, no separate vendors.

Layer 01

Compute Power Layer

The foundation: make every GPU work harder

GPU Management Partitioning Heterogeneous Scheduling

Multi-engine support: Deploy AI workloads on bare metal, VMs, or containers within the same platform.
1% GPU granularity: Precise allocation down to 1% increments — dramatically reduces waste.
GPU passthrough at 95% performance: Full physical performance for demanding training workloads.
vGPU partitioning: Share GPU resources across teams without per-seat licensing from GPU vendors.
Heterogeneous scheduling: Unified management across multi-brand, multi-architecture GPU pools.
Real-time monitoring + self-healing: Resource utilization visible at all times; failures auto-recover.

Layer 02

Model Layer (AI MaaS)

From raw compute to running models — without the complexity

Training Evaluation Inference RAG App Deployment

Full lifecycle MaaS: Model training → evaluation → inference → updates, all managed through one platform.
Intelligent task decomposition: AI tasks dynamically broken down, routed, and scheduled for optimal resource use.
Distributed parallel training: Scale training jobs across multiple nodes with adaptive load balancing.
Model compression & optimization: Efficient deployment with adaptive scheduling between training and inference.
Broad model support: Generative AI, NLP, computer vision, multimodal — hundreds of large models supported.
RAG knowledge base: Local retrieval-augmented generation with multiple orchestration strategies and plugin integration.

Layer 03

Operational Layer

Governance, visibility, and reliability for enterprise AI at scale

Scheduling Billing Multi-tenant HA Security

Cross-platform metering & billing: On-demand billing across multiple GPU clusters, compute centers, and tenants.
Visual unified portal: Comprehensive, intuitive view of all AI resources across the entire infrastructure.
Elastic fault tolerance: Rapid failure localization and self-healing; cross-platform DR with minimal RTO.
Multi-tenant isolation: Resource quota management per team, project, or business unit.
Sensitive data detection: End-to-end data security — file-level isolation and localized data management.
High availability for AI: Elastic fault-tolerant self-healing module maintains service continuity for production workloads.

Product Advantages

Built for enterprise AI.
Not retrofitted.

Every design decision optimized for production private AI — not adapted from general-purpose infrastructure.

Low Barrier to Entry

Minimum 2-node deployment. Full platform capabilities from day one. No need to build a full GPU cluster before experimenting.

One-Stop AI Experience

Data management → model training → inference → app deployment. One platform, one interface, no integration projects.

High Cost-Effectiveness

Dynamic GPU partitioning maximizes hardware utilization. The same GPU cluster serves more teams, more workloads, with less waste.

High Performance

95% GPU passthrough performance for training. High-performance storage network optimized for AI I/O patterns. Adaptive load balancing for inference.

Security & Data Sovereignty

Localized data management. File-level isolation. HA and DR built in. Your models, your data, your infrastructure — entirely under your control.

Use Cases

Any scale. Works with the GPUs you already have.

ZStack AIOS supports heterogeneous GPU environments, eliminating the need
to standardize on a single vendor before running enterprise AI.

Scenario 01

Model Training and Fine-Tuning

Build AI that understands your industry

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.

Fine-tuning Foundation Models Industry-specific

Scenario 02

Model Inference at Scale

Deploy AI into production without cloud dependency

Run inference workloads for production AI applications using on-premise GPU resources. Dynamic scheduling ensures inference SLAs are met even as demand fluctuates, while keeping all data on your own infrastructure.

Production Inference Dynamic Scheduling SLA Guaranteed

Scenario 03

AI Application Deployment

Go from model to application in your own environment

Enable local implementation of RAG knowledge base applications. Support multiple inference service orchestration strategies and plugin integrations. Quickly deploy AI applications — chatbots, document analysis, vision systems — without sending data to external APIs.

RAG Chatbots Document Analysis Vision Systems

Compatibility

Works with your existing infrastructure.

No rip-and-replace. ZStack AIOS overlays on the GPUs you already own
and the ZStack platforms you already run.

Heterogeneous GPU Support

Multi-brand, multi-architecture GPU pools unified under a single scheduling layer. Legacy and new hardware work together — no silos.

NVIDIA

H100 / A100 / A800 / RTX series

Supported

昇腾

昇腾 Ascend

910B / 910A / Atlas series

Supported

Other AI Chips

Mainstream domestic & international AI accelerators

Compatible

Unified management across all GPU types — heterogeneous scheduling is built in, no extra configuration needed.

Platform Integration

Designed to overlay on existing ZStack platforms — inherits all services, docs, and support with no re-platforming required.

ZStack Cloud Foundation

Full enterprise cloud infrastructure

ZStack Virtualization Foundation

Enterprise-grade virtualization

ZStack HCI

Hyper-converged infrastructure

+ ZStack AIOS overlay

Private AI infrastructure — no separate system needed

For existing ZStack customers: AIOS overlays directly on your existing platform. No need to build a separate AI system.

Customer Stories

Trusted in production.

Real deployments across finance, healthcare, and government — where data sovereignty is non-negotiable.

Education

""Migrated our entire VMware environment to ZStack in six weeks — without disrupting a single teaching system. The parallel-operation feature was critical to our risk management.""

Regional University

Education · 800+ VMs migrated · Cost −65%

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Education

"We migrated our core transaction systems from VMware to ZVF in 90 days with zero downtime. The ZMigrate tooling made what we feared would be a 12-month project feel completely manageable."

Financial Services · Core Banking

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Financial

""ZStack's CDP gave us the second-level RTO/RPO guarantees our regulators demand — at less than a third of what we paid for the legacy solution. Data never leaves our premises.""

Regional Commercial Bank

Financial Services · RPO <5s · TCO −58%

Read case study

View more Customer Stories

Run Enterprise AI On Your Infrastructure. Under Your Control.

Why enterprises needPrivate AI infrastructure

One platform. Three layers.

Built for enterprise AI.Not retrofitted.

Any scale. Works with the GPUs you already have.

Works with your existing infrastructure.

Trusted in production.

Your enterprise AI strategystarts here.

Run Enterprise AI
On Your Infrastructure.

Under Your Control.

Why enterprises need
Private AI infrastructure

Built for enterprise AI.
Not retrofitted.

Your enterprise AI strategy
starts here.