Lilly Tech Systems
Networking for Hybrid Cloud AI
Connect on-premises GPU clusters to cloud services using dedicated interconnects, VPNs, and service mesh architectures optimized for AI data transfer patterns.
Connectivity Options
| Option | Bandwidth | Latency | Cost | Best For |
|---|---|---|---|---|
| AWS Direct Connect | Up to 100 Gbps | Consistent low | High (dedicated) | Large data sync, distributed training |
| Azure ExpressRoute | Up to 100 Gbps | Consistent low | High (dedicated) | Azure ML integration |
| GCP Interconnect | Up to 100 Gbps | Consistent low | High (dedicated) | Vertex AI hybrid |
| Site-to-Site VPN | 1-10 Gbps | Variable | Low | Model sync, API access |
| SD-WAN | Variable | Optimized | Medium | Multi-site with QoS |
Bandwidth Planning for AI
AI workloads have unique bandwidth requirements that differ from typical enterprise traffic:
Dataset Transfer
Bulk transfer of TB-scale datasets. Bursty pattern, often scheduled off-peak. Needs high sustained throughput, latency less critical.
Model Sync
Bidirectional model artifact transfer (MB-GB). Triggered by training completion. Needs reliability more than raw speed.
Distributed Training
Gradient synchronization across on-prem and cloud GPUs. Requires ultra-low latency (<5ms) and high bandwidth. Only viable with dedicated interconnects.
Service Mesh for Hybrid AI
A service mesh like Istio or Linkerd provides consistent service-to-service communication across on-premises and cloud Kubernetes clusters. For AI workloads, this enables transparent routing of inference requests between environments.