How to Connect a Private GPU Cloud to Your Enterprise Network

Teams evaluate private GPU clouds on GPUs and pricing, then discover the real project is the wire: getting terabytes of training data in, results out, and users connected — without turning the enterprise network into the bottleneck or the audit finding. Connectivity is an architecture decision with three path families, and choosing by default (usually a VPN because it exists) costs more than choosing deliberately. This page compares the three paths on the dimensions AI traffic actually stresses.

Quick Answer: How Do You Connect a Private GPU Cloud to Your Enterprise Network?

Three path families connect an AI enclave to your network: encrypted transport over the public internet (no procurement, internet latency), private virtual tunnels over provider backbones (logical isolation, predictable latency), and dedicated physical interconnect (deterministic bandwidth, circuit economics). AI traffic splits three ways — dataset sync is throughput-bound and tolerates internet paths, training coordination is latency-sensitive, inference serving is latency-tail-bound — so most estates run more than one path. Inside a OneSource Cloud enclave, every path hands off to an isolated Spine-Leaf RoCE v2 fabric at the enclave edge, which keeps the deterministic part deterministic.

The Three Paths: Encrypted Internet, Private Tunnel, Dedicated Interconnect

Three path families connect an AI enclave to the enterprise: encrypted transport over the public internet, which needs no procurement but shares fate with internet routing; private virtual tunnels over provider backbones, which isolate traffic logically with predictable latency; and dedicated physical interconnect, which delivers deterministic bandwidth at the cost of circuit procurement and port economics. Most enterprises run more than one path in steady state, sized to traffic class rather than picking a single winner.

Product names obscure this structure — every provider brands its tunnel and interconnect products differently — but the mechanism families are stable, and so is the decision logic. Start by naming which path you have today and which traffic class it carries; the mismatch between them is usually the first finding of any connectivity review.

Trade-Offs for AI Traffic: Sync, Coordination, and Serving

AI traffic splits into three patterns with different sensitivities: dataset and checkpoint sync is throughput-bound and tolerates latency, making encrypted internet or tunnel paths adequate when the transfer window is flexible; distributed training coordination is latency- and jitter-sensitive, favoring dedicated paths when clusters span the connection; and inference serving is latency-bound with steady volume, where tunnel or dedicated paths protect tail latencies. Security evidence differs per path too — public transport carries encryption duty entirely on your stack, while private paths split evidence with the provider and produce cleaner audit narratives for regulated data in transit.

The security-evidence split matters most for regulated estates. On public transport, your encryption stack is the whole story, and auditors will want configuration exports and key-management evidence from your side. On private paths, the provider shares the duty and typically documents backbone isolation in audit-ready form. For workloads where data-in-transit evidence features in reviews, that documentation difference alone has justified tunnel upgrades — the circuit costs less than the evidence project it replaces.

Choosing and Operating the Path: Cost and Verdict

Choose encrypted internet for exploratory and burst sync workloads where cost dominance rules; choose private tunnel paths for steady inference and mixed estates needing predictable latency without circuit procurement; reserve dedicated interconnect for cross-site distributed training or regulated data flows whose compliance narrative benefits from physical separation. Running cost differs structurally: internet paths cost egress and ops attention, tunnels add provider circuit fees, and dedicated interconnect adds port-hour and cross-connect charges — model twelve months of each against the workload's sensitivity before committing.

Whatever path you choose, its quality is bounded by the fabric waiting at the enclave edge. OneSource Cloud enclaves terminate enterprise connectivity at the boundary of an isolated Spine-Leaf RoCE v2 network with priority flow control — the deterministic behavior that distributed training and tail-latency-sensitive inference depend on lives inside the tenancy boundary, so your path selection preserves rather than creates it. Model your three traffic classes against the three paths, price twelve months of each, and the right answer is usually two paths: one cheap and flexible for sync, one private for the traffic that cannot afford jitter. The enclave-side reference is documented at OneSource Cloud AI Cluster Networking.

FAQ

Does inference traffic justify paying for a dedicated interconnect?

Only when tail-latency guarantees are contractual or user-facing. Steady internal inference usually meets its targets on a private tunnel path, while dedicated circuits earn their cost when serving latency is revenue-critical or when training coordination spans the link. Price the twelve-month difference against the cost of one latency-driven incident and the ranking usually decides itself.

How do tenants connect into a OneSource Cloud enclave?

OneSource Cloud enclaves terminate enterprise connectivity at the enclave edge, where the isolated RoCE v2 fabric takes over. Tenants bring encrypted internet, private tunnel, or dedicated interconnect per traffic class, and the enclave-side fabric guarantees deterministic behavior inside the single-tenant boundary — the path choice stays yours; the determinism inside is contractual.