DISTRIBUTED INFRASTRUCTURE

Distributed AI

Deploy AI models globally. Process locally. Scale infinitely.

Multi-region GPU infrastructure with seamless orchestration. Run inference at the edge, train in the core, and deliver AI experiences with sub-15ms latency worldwide.

Why Distributed AI

The Future is Distributed

By 2030, 74% of global data will be processed outside traditional data centers. Edge AI market projected to reach $66.47B by 2030.

<15ms

Inference Latency

Process AI at the edge for real-time responses

75%

Data at Edge

Enterprise data created at the edge by 2025

10x

Cost Reduction

Lower bandwidth costs with local processing

99.99%

Uptime SLA

Multi-region redundancy and failover

Global Distributed Architecture

AI inference at the edge, training in the core, orchestrated globally

Edge Nodes

Singapore, Tokyo, Mumbai

T4/A100
GPU Types
5-15kW
Power/Site
<10ms
Latency
1-8 GPU
Per Node

Regional Hubs

Jakarta, Sydney, Seoul

A100/H100
GPU Types
200kW-2MW
Power/Site
<25ms
Latency
50-500
GPU Count

Core Cloud

Singapore Tier-3 DC

H100/B200
GPU Types
>10MW
Power/Site
Training
Workload
1000+
GPU Count

Data flows seamlessly between edge, regional hubs, and core cloud

InfiniBand NDR

400 Gbps inter-cluster

Multi-Cloud Transit

AWS/Azure/GCP peering

Kubernetes Fleet

Unified orchestration

Key Capabilities

Enterprise-Grade Distributed AI Platform

Multi-Region Orchestration

  • Kubernetes Fleet Management across 15+ locations
  • Automated model deployment and versioning
  • Global load balancing with geo-routing
  • Cross-region model synchronization
  • Centralized monitoring and observability

Edge Inference Acceleration

  • NVIDIA T4, A100, H100 at edge locations
  • TensorRT optimization for 3x faster inference
  • Model quantization (FP16/INT8) support
  • Triton Inference Server with batching
  • Sub-15ms P99 latency guarantee

Security & Compliance

  • Data residency compliance (GDPR, PDPA)
  • End-to-end encryption in transit and at rest
  • Zero-trust network architecture
  • Integrated Firewall for AI protection
  • SOC 2 Type II certified infrastructure

Hybrid Training Pipeline

  • Train centrally on H100/B200 clusters
  • Deploy models to edge automatically
  • Federated learning support
  • Edge data aggregation for retraining
  • A/B testing across regions

Observability & Monitoring

  • Real-time metrics across all nodes
  • Distributed tracing with OpenTelemetry
  • GPU utilization and cost analytics
  • Automated anomaly detection
  • Custom dashboards with Grafana

Auto-Scaling & Cost Optimization

  • Traffic-based auto-scaling per region
  • Spot instance integration for training
  • Request routing to nearest available GPU
  • Idle resource hibernation
  • Cost attribution by project/team
Use Cases

Real-World Applications

Retail & E-Commerce

Deploy product recommendation models at edge stores and distribution centers. Process customer behavior locally, sync insights globally.

Challenge: 200ms cloud latency killed conversion
Solution: T4 GPUs in 50+ stores
Result: 12ms inference, 18% conversion lift

Video Analytics & Surveillance

Real-time object detection and facial recognition at camera sites. Train models centrally on aggregated footage.

Challenge: 10 TB/day uplink for 1000 cameras
Solution: Edge inference + smart upload
Result: 95% bandwidth savings

Healthcare & Diagnostics

Deploy medical imaging models to hospitals while maintaining data residency. HIPAA-compliant distributed inference.

Challenge: Patient data cannot leave country
Solution: In-country edge nodes + federated learning
Result: 100% data compliance, 8ms diagnosis

Gaming & Metaverse

Distributed AI NPCs, real-time content generation, and anti-cheat detection at regional hubs nearest to players.

Challenge: 100M+ MAU across APAC
Solution: Regional A100 clusters
Result: <20ms global AI response time
Technology Stack

Built on Best-in-Class Components

Compute

  • NVIDIA H100, A100, T4 GPUs
  • AMD EPYC 9004 series CPUs
  • NVMe SSD storage arrays
  • 400G InfiniBand networking

Orchestration

  • Kubernetes 1.28+ with GPU operator
  • KubeFlow for ML pipelines
  • ArgoCD for GitOps deployment
  • Istio service mesh

AI Runtime

  • NVIDIA Triton Inference Server
  • TensorRT for optimization
  • ONNX Runtime
  • vLLM for LLM serving

Observability

  • Prometheus + Grafana
  • OpenTelemetry tracing
  • ELK stack for logs
  • DCGM for GPU metrics
Integration

Works with Your AI Stack

🤗
HuggingFace

Model Hub Integration

🦜
LangChain

Agent Frameworks

🦙
LlamaIndex

RAG Pipelines

vLLM
vLLM

Inference Engine

PyTorch
PyTorch

Training Backend

TensorFlow
TensorFlow

ML Framework

Qwen
Qwen

Alibaba LLM

Anthropic
Anthropic

Claude AI

Seamless Deployment

# Deploy your model to distributed edge
artglobal deploy \
  --model huggingface/llama-2-7b \
  --regions asia-southeast,asia-east,oceania \
  --gpu-type a100 \
  --replicas 3 \
  --autoscale-max 10

# Automatic deployment to:
# - Singapore (primary)
# - Tokyo (secondary)
# - Sydney (tertiary)

Ready to Go Distributed?

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