Case Studies

Enterprise AI Operating System

Provincial AI Computing Power Dispatch Center

This provincial computing center adopted FIM One as its unified AI orchestration platform, achieving centralized management of "hundreds of models" and resource pooling across heterogeneous chips.

Key Metrics

Business Impact

0+
Managed Models
Covers full-stack AI including LLM, CV, and NLP
0%
Utilization
Significantly improved compute utilization via dynamic peak-shaving
0.9%
SLA
Enterprise-grade high availability ensuring continuous operation
Comprehensive
Unified Control
Successfully broke AI silos across the regional infrastructure
Core Technology Features

Technical Highlights

Heterogeneous Pooling

Breaks chip barriers, enabling mixed scheduling of localized and general-purpose compute

Model Service Mesh

Istio-based microservice governance for fine-grained traffic control and second-level recovery

AI Security Gateway

Built-in Prompt injection defense and data de-identification for AI-era security

Dynamic Admission

Data-driven automated model evaluation helping select appropriate models for deployment

CVNLPMLDLLLMRL100+APISync7x24
Adoption Overview

Customer Context

This Provincial AI Computing Power Dispatch Center coordinates regional resources for government, research, and public AI applications. With rising localization requirements, the center faced a complex mix of general GPUs and domestic AI chips. Legacy siloed architectures prevented resource pooling and cross-chip model migration, creating an urgent need for an AI Operating System to shield hardware differences.

Technology Stack

KubernetesIstiovGPUPrometheusOpenTelemetry
Transformation

From Pain Points to Adoption

Transformation
1Compute silos: Heterogeneous chips (GPU/NPU) couldn't be scheduled together, leading to expensive resources staying under 20% utilized
Turned on the Heterogeneous Compute Virtualization Engine to shield chip differences, achieving unified pooling and scheduling of general and domestic chips
2Migration barriers: Differing drivers and frameworks across vendors made the cost of migrating models across chips extremely high
Leveraged FIM One's Model Service Mesh for intelligent traffic routing, enabling smooth migration and backup across localized chips
3Service governance gaps: Lack of unified traffic orchestration and circuit breaking led to poor stability for model services under peak loads
Enabled the AI Application Security Gateway, combining full-link monitoring and content risk plugins to expose standardized secure inference APIs
4Security admission ambiguity: Massive model access lacked unified security auditing and compliance risk control, posing data and content risks
Used the Automated Evaluation Pipeline for dynamic performance assessment of models, ensuring precise allocation of compute resources
Technical Architecture

System Architecture Design

Layer 1
Resource Abstraction Layer

Abstracting underlying chip differences (General/Domestic Chips) for unified resource pooling and scheduling

Heterogeneous MgmtPoolingAuto-scalingvGPU
Layer 2
Model Service Mesh

Service Mesh based model traffic governance supporting A/B testing, canary release, and circuit breaking

RoutingCircuit BreakingCanaryOrchestration
Layer 3
AI Application Gateway

Enterprise-grade unified API access with auth, rate limiting, billing, and full-link observability

Unified APIAuthObservabilityBilling
异构算力调度模型服务网格全链路观测资源配额控制
Adoption Journey

Phased Implementation

1
Evaluation

Infrastructure Pooling

The center brought all GPU/NPU resources onto FIM One with unified virtualization, establishing the heterogeneous scheduling foundation

2
Pilot

Service Governance Pilot

The center turned on Model Mesh for regional AI traffic, validating multi-tenant isolation and dynamic rate limiting

3
Scale-out

Ecosystem Scale-out

The center rolled out the AI App Gateway and Developer Center, offering one-stop AI capability invocation to all provincial agencies

Testimonial

Customer Voice

This platform solved our urgent need of "having compute but failing to schedule". It not only shielded the complexity of different chips but also nearly doubled our resource utilization, truly achieving centralized regional management.

Center Chief Engineer

Provincial Digital Transformation Expert

FAQ

Frequently Asked Questions

How does the platform solve domestic chip adaptation issues?
What are the advantages of Model Mesh over traditional gateways?
Does the platform support public cloud LLM integration?

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