The GPU Operations Responsibility Split Across Delivery Models

Ask two managed GPU providers who patches the scheduler and who holds the pager at 3 a.m., and you will get two different answers wearing the same service name. Cloud shared-responsibility models taught the industry to ask the question — but those models are security-framed and generic, while GPU operations has its own activity set, and NVIDIA's cloud-partner documentation now defines a reference split specific to GPU infrastructure. What buyers need is the matrix: activity by activity, across delivery models, with the seams where gaps become incidents.

Defining the Split: What Responsibility Covers in GPU Operations

GPU operations responsibility is the assignment of eight recurring activities — monitoring, patching and updates, scheduling and quota policy, capacity management, incident response, security operations for the infrastructure layer, backup and recovery of infrastructure state, and cost governance — to the party that performs and is accountable for each. NVIDIA's cloud-partner documentation anchors the reference split for GPU infrastructure: the provider owns the infrastructure stack, the tenant owns applications and their configuration, and everything interesting sits in the negotiated middle where the contract either names an owner or an incident will.

The eight-activity vocabulary is deliberately operations-level rather than security-level: the canonical responsibility models answer who is accountable for securing layers, which matters, but incidents and audits ask who performed work — and performed-work questions are operations questions. The vocabulary also travels: it works identically for self-run colocation, managed dedicated enclaves, and provider-operated platforms, which is what makes the matrix comparable across quotes.

The Matrix by Delivery Model

Self-run assigns all eight activities to the tenant — the control maximum and the staffing bill that comes with it; provider-operated platforms assign the infrastructure stack to the provider with tenant ownership of workload configuration; and the managed dedicated model splits deliberately: the provider operates the substrate — facility, bare-metal nodes, fabric, and storage, the layer OneSource Cloud runs with 24/7 coverage in U.S. data centers — while the tenant keeps scheduling policy, workload security, and cost governance, with jointly-run incident response bridging the two. The matrix matters because the same headline service name can hide different rows: two managed offerings differ in who patches the scheduler and who holds the pager at 3 a.m.

Read the matrix against any quote you hold: for each of the eight rows, ask the provider to point at the contract line that assigns it. Rows without a line are not unassigned — they are assigned to whoever discovers them during the incident, which is the most expensive assignment in operations.

Boundaries, Escalation, and Evidence

The split fails at three predictable seams: the incident handoff (tenant sees workload symptoms, provider holds infrastructure telemetry — without a joint escalation path both sides wait), the patch window (provider patches infrastructure, tenant owns workload tolerance — uncoordinated windows become self-inflicted outages), and the evidence gap (auditors ask for records crossing both sides — incident timelines, patch logs — and responsibility without evidence export fails the review). The boundary discipline is one escalation path crossing both NOCs, a shared maintenance calendar, and evidence-export terms in the contract; the managed model earns its premium precisely by making these three seams contractually explicit.

The managed dedicated row of the matrix is where these seams close best, because the model's whole product is the explicit split: substrate operations under provider coverage — the 24/7 layer that OneSource Cloud operates across bare-metal nodes, RoCE v2 fabric, and NVMe-oF storage in U.S. data centers — with tenant policy planes above it and flat-rate monthly pricing that keeps the cost-governance row legible. The reference delivery is documented at OneSource Cloud Managed AI Infrastructure.

FAQ

Who owns incident response at 3 a.m. on managed GPU infrastructure?

Both sides, by design: the provider's NOC owns infrastructure telemetry and remediation — the managed model OneSource Cloud delivers includes provider-side operations coverage for the substrate — while the tenant's on-call owns workload symptoms and the go/no-go on failover, joined by one escalation path with a contractual response time; if the contract names only one side, the seam between them is your next incident's root cause.

What does the tenant still own under a fully managed GPU model?

The workload layer and the decisions: scheduling policy and quotas, workload security above the substrate, model and data artifacts, cost governance, and the acceptance of maintenance windows — the managed model transfers substrate operations, never accountability, and OneSource Cloud structures the split so tenant control planes ride on a provider-operated substrate with flat-rate monthly pricing.