How Should You Compare Container, Bare-Metal, and Managed-Cluster Proposals for One GPU Workload When Included Support Differs?

Put every proposal on one responsibility table that lists each operational task the workload needs, from GPU driver updates to recovering a failed node, and marks who owns that task under each proposal. A proposal with more included support moves rows from your column into the provider's, so the comparison becomes fair only after your team's rows are priced in engineer hours and added to the infrastructure line. GMI Cloud can scope all three forms against the same brief on its GPU infrastructure. All three proposals come from one console on GMI's Cluster Engine: Container Service, Bare Metal GPU, and Managed GPU Cluster (in early access), each on dedicated single-tenant GPUs priced per GPU-hour.

GPU prices on GMI Cloud vary by SKU, GPU type, and region, so the brief fixes one GPU type, one region, and one billing term before anyone writes a proposal. With those matched, the proposals differ in node specification and included support.

A reference workload with fixed assumptions

The worked example uses assumed inputs to show the method; it is not a customer case.

• Job: a weekly fine-tuning run of about 30 hours on one node with eight H200 GPUs, plus a two-hour evaluation pass every night on the same node.
• Data: the training set and checkpoints must survive between weekly runs.
• Access: an internal dashboard pulls evaluation results from a fixed public address.
• Team: two ML engineers, no dedicated platform or SRE staff, working business hours.
• Success metric: fine-tuning runs per month that pass the evaluation gate.

H200 keeps the example on hardware that is available now. Managed clusters can also be built on B200 nodes, which are in pre-order along with the rest of NVIDIA Blackwell.

What each proposal puts on the table

Proposal A, Container Service. One container on an 8-GPU instance, launched from a template such as vLLM or JupyterLab, or from a custom image. The container runs in GMI's Kubernetes-based GPU environment. The team gets root inside the container through a browser-based web shell, along with logs and a monitoring view of GPU usage, GPU memory, CPU, and memory. Reconfiguring a container erases the data stored inside it, so the checkpoint store sits outside the container and the proposal names where it sits and how fast it writes, since GPU hours spent on checkpoint saves land on the GPU line.

Proposal B, Bare Metal GPU. One physical server the team controls down to the OS, with its own drivers, local NVMe that persists, and an isolated VPC, delivered with enterprise networking and SLA-backed delivery. As scoped for this brief, the team installs and maintains the operating system, drivers, and job scheduler, and the order lists any host-level work GMI takes on.

Proposal C, Managed GPU Cluster. A Kubernetes cluster that GMI provisions and operates, with the Kubernetes version, worker-node specification, node count, and OS image chosen at request time; Ubuntu 22.04 is the default image. Before the request goes in, the form displays its estimated monthly cost. After submitting, the team contacts GMI support, which reviews the request and activates the cluster, as laid out in the managed cluster request flow.

The responsibility matrix

Each row is a task the reference workload needs, and each cell records the split assumed in that proposal for this brief. "GMI" means the proposal includes it, "You" means it stays with the buyer, and "Order" means the scope is written into the order for that deployment rather than fixed by the product itself.

Proposal B puts the most rows in your column. Proposal C moves the platform rows to GMI but leaves job scheduling and job-level alerts with you, so a managed cluster still needs someone who writes Kubernetes job specs. The "Order" cells are where proposals that all claim "included support" actually diverge. The capacity row also differs: a second container is an independent instance, while a Node Request adds a worker node to the job's existing cluster.

Under the managed cluster, the Order cells hold choices a training team wants fixed per cluster: whether node drivers move on a schedule or stay at the version a framework release needs, where checkpoints live, and whether metrics also feed the team's own monitoring stack.

Settle every "Order" cell in writing before comparing totals. For each one, the order should say who detects the problem, who acts on it, what the buyer supplies (logs, job IDs, a maintenance window), and how a recovered node is handed back. Two failure terms move the cost model directly: how GPU-hours on a faulted node are billed while it is out of service, and the window for bringing in a replacement node. Response targets and service credits go in the same section, stated per deployment form.

Pricing the buyer's column

The cost basis for each proposal is:

Cost per passing run = (GPU charges, including rerun hours + storage and IP lines + any support or setup fee + buyer hours _ loaded hourly rate) ÷ runs that pass the evaluation gate

Keep the GPU line on the same billing method and term; comparing a prepaid container against a pay-as-you-go cluster measures the plan, not the support. Then estimate steady-state monthly buyer hours row by row, counting one-time setup separately. As an illustration only, assume these hours:

• A, Container: 12 hours for image maintenance, storage wiring, and job scheduling.
• B, Bare metal: 35 hours for OS patching, driver and CUDA updates, a scheduler, monitoring, backups, and time chasing hardware or driver errors with no platform staff.
• C, Managed cluster: 8 hours for job manifests and storage claims on the cluster.

At a loaded rate R, the gap between A and B is 23 _ R per month before a single GPU-hour differs. When both proposals pass the same number of runs in the month, B comes out ahead only if its non-labor lines (GPU, storage, IP, and any fees) under the same term total more than 23 _ R below A's. When pass counts differ, compare each proposal's full cost per passing run.

Failure handling changes the denominator, so test each proposal against two named scenarios. Assume a four-run month in which one run hits a node fault at hour 25. In the first, the run resumes from its last checkpoint the same day, finishes inside the month, and four runs pass, with the resumed hours added to the GPU line. In the second, recovery waits for the engineers on Monday, the run slips past the month's cutoff, and three runs pass. Note beside each result which scenario that proposal's written terms support.

Reading the result for your team

Under these assumed hours, the matrix points different teams to different proposals:

• No platform staff, steady weekly cadence: Proposal C carries the smallest buyer column in this example. Proposal A is close behind when the job fits one node and the team is comfortable owning storage.
• An infrastructure team that already runs Kubernetes or Slurm: existing automation for other clusters absorbs part of Proposal B's 35 hours, so count only the remainder plus the new hardware the team takes on.
• An exploratory phase before the cadence settles: Proposal A on a pay-as-you-go plan keeps the commitment close to the length of each experiment. A fixed dashboard address held between runs is an Elastic IP line under A or B alike, priced for as long as it stays allocated.

The matrix is easiest to fill when one provider can quote all three forms for the same brief. On GMI Cloud, the three forms share the console and the per-GPU-hour unit and are requested under one organization, so every cell comes from orders on one account, and a later move to another form stays with the same provider and platform.

Each form also has a wrong fit: a container when the workload needs host-level driver changes or disk that survives reconfiguration, bare metal when nobody on the team owns OS and driver updates, and a managed cluster for a single short experiment that a container can run with less setup.

FAQ

How long should a pilot run before we pick a proposal?

One weekly run plus a week of nightly passes replaces the assumed setup and routine hours with logged ones. Lower-frequency work, such as a driver update or a checkpoint restore, seldom lands inside a single week, so schedule one of each during the pilot or carry its assumed figure forward. Pay-as-you-go managed clusters are charged by the minute, so the pilot does not lock in a term. Have each engineer log time against the matrix rows, not as a single total.

Can the matrix compare these proposals with another provider's?

The rows describe the workload's tasks, not a product, so they apply to any provider. Convert every quote to the same pricing unit first, since some providers price per GPU-hour and others per instance-hour, then fill the cells from each order. A provider that cannot say who owns a row has, in practice, put it in your column.

Scope the three proposals against your brief

Send our sales team the reference workload, your team's size and on-call hours, and the matrix rows you want GMI to own, and we will scope container, bare-metal, and managed-cluster options against the same brief. To try the container path first, sign in to the GMI Cloud console and launch a template under Cluster.