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Choose a neocloud as an end-to-end production platform, not as a discounted GPU-hour listing. Verify the exact accelerator and topology, run your own workload benchmarks, inspect the provider’s SLA and security evidence, measure storage and network behavior, and rehearse failure and exit procedures. A short, production-like pilot should decide the contract—not an advertised GPU model or a marketing benchmark.
What a production evaluation must prove
A neocloud may supply bare-metal servers, virtual machines, managed Kubernetes, or a mixture of these. Your application experiences all of the surrounding systems: scheduler queues, host CPU and memory, GPU interconnects, storage, network paths, drivers, firmware, identity controls, monitoring, support and billing. A provider can offer the right GPU yet deliver unacceptable tail latency, checkpoint times or job reliability because another part of the stack is constrained.
Define acceptance criteria before requesting quotes. State the model architecture, precision, batch sizes, sequence lengths, concurrency, training duration, checkpoint frequency, data location, latency objective, region, compliance obligations and expected growth. Translate those requirements into measurable outcomes such as tokens per second, samples per second, p95 and p99 latency, scaling efficiency, time to first useful job, checkpoint and restore time, completed jobs per day, and cost per useful output.
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Decide whether the workload is sensitive to topology
Distributed training and low-latency inference often depend more on communication paths than on the nominal GPU name. Record GPU-to-GPU links, PCIe or NVLink topology, NUMA placement, host CPU and RAM, network fabric, RDMA support, oversubscription and whether GPUs are dedicated or shared. Ask for a node diagram and a command-level way to inspect topology, rather than accepting a generic server specification.
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- Training: confirm all-reduce bandwidth, inter-node latency, RDMA behavior, congestion controls, and scaling efficiency as the cluster grows.
- Inference: confirm placement of model shards, GPU peer access, network path to clients, and p95/p99 latency under the promised concurrency.
- Data-heavy jobs: confirm local scratch performance, metadata behavior, object-store access and checkpoint bandwidth.
Build a hardware and platform profile
Require a written profile for every capacity type you may purchase. The profile should identify the exact GPU SKU, memory capacity and bandwidth, host CPU model and allocated cores, system RAM, local disks, network interfaces, virtualization mode, and firmware and driver versions. “A100-class” or “H100 equivalent” is not precise enough for a production decision.
GPU, memory and interconnect
Capture GPU memory size, usable memory after system reservations, ECC behavior, clock policy, power limits and MIG or other partitioning options. Ask whether a supposedly dedicated GPU shares a host, PCIe root complex, power budget or network uplink with other tenants. For multi-GPU jobs, obtain the actual topology for the offered node shape and test peer-to-peer transfers.
Virtual machines versus bare metal
Virtualization can improve flexibility but introduces questions about passthrough, vGPU partitioning, noisy neighbors, reboot behavior and access to performance counters. Bare metal may provide predictable performance while offering slower provisioning or less automation. Have the provider state which controls are available in each mode, how host maintenance is handled, and whether a failed host is replaced with an identical shape.
Scheduler and Kubernetes behavior
Ask how GPUs are requested, allocated, preempted and released. Confirm quota units, queue policy, gang scheduling for distributed jobs, placement constraints, reservations, autoscaling and priority classes. If you use Kubernetes, verify the supported version, device-plugin behavior, GPU health reporting, node labels, taints, admission controls and upgrade policy. Test that a failed GPU is drained and replaced without silently leaving a job in a corrupted state.
Demand reproducible performance evidence
Do not accept a single throughput number without its conditions. Request benchmark scripts, commit or release versions, datasets, driver and CUDA versions, precision, batch size, sequence length, parallelism, warm-up procedure, number of GPUs, network topology, failure assumptions and measurement method. NVIDIA’s Exemplar Cloud initiative describes a standardized framework for NVIDIA Cloud Partners to demonstrate real-world AI workload performance and resiliency; use that kind of detail as the minimum standard for a comparable result.
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Metrics that map to business output
| Workload area | Measure | Why it matters |
|---|---|---|
| Training | Samples or tokens per second; time per step; scaling efficiency; completed steps per dollar | Reveals communication overhead and the cost of reaching a target model quality |
| Inference | Tokens per second; time to first token; p50, p95 and p99 latency; throughput at target concurrency | Exposes tail behavior that an average latency hides |
| Data path | Dataset staging time; cache warm-up; read/write throughput; metadata operations | Shows whether GPUs will wait for storage |
| Reliability | Startup success rate; job completion rate; checkpoint and restore time; recovery after node or GPU failure | Quantifies lost work and operational risk |
| Economics | Cost per useful token, completed job, training step or request | Connects the invoice to the result you actually sell or use |
Re-run tests with your model and data movement
Run the provider’s script unchanged, then run your own container, model and representative data. Keep the same driver, CUDA, framework and kernel versions across candidates. Record queue time separately from provisioning time and application runtime. Repeat at the concurrency and utilization you expect in production; an idle cluster can produce misleadingly good latency.
For a distributed job, compare one node, two nodes and the largest planned shape. Plot scaling efficiency, not just total throughput. If throughput falls sharply when another tenant or job is present, ask whether capacity can be isolated and whether that isolation is contractual. NVIDIA DGX Cloud materials describe validated hardware and software stacks and performance-per-total-cost-of-ownership benchmarking; request equivalent evidence for the specific service you are buying.
Test storage and network paths as first-class dependencies
Measure the complete data path from your source system to the GPU, not only the advertised storage tier. Stage a production-sized dataset, warm the cache, write checkpoints at the intended frequency, restore them, and repeat under concurrent load. Include metadata-heavy operations such as listing millions of files if your training pipeline performs them.
Questions for the storage design
- Is the data on local NVMe, a network filesystem, block storage, or object storage?
- Who operates the storage service and its control plane?
- What bandwidth and IOPS are provisioned per tenant, node and cluster?
- How do snapshots, versioning, replication and point-in-time recovery work?
- What is the egress path and charge for moving a full dataset or checkpoint history out?
- What deletion certificate or audit evidence is supplied when data is removed?
NVIDIA’s AI-cloud requirements call for requested bandwidth and IOPS provisioning and define availability and durability expectations for storage services. The 2026 reference requirements cite a 99.99% high-speed-storage, 30-day rolling service-level objective and durability above 99.999% per petabyte. These are reference requirements, not universal neocloud guarantees; obtain the provider’s own measured and contracted values.
Check the external network
Measure ingress, egress, cross-zone and cross-region paths from the locations your users and data sources actually occupy. Record sustained bandwidth, latency, packet loss and p95/p99 behavior during busy periods. For RDMA, verify that the advertised capability is available to your tenancy, not merely present somewhere in the provider’s fabric. Include firewall, private-link or VPN setup time in your pilot because a fast GPU is of little use while data waits for a network change.
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Evaluate reliability, maintenance and support
A useful SLA states what is measured, where, how often and what remedy follows a breach. Require definitions for availability scope, measurement interval, maintenance exclusions, incident severities, response and restoration targets, customer reporting, service credits and termination rights. Distinguish control-plane availability from GPU capacity, storage, networking and your application’s availability.
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NVIDIA’s 2026 reference sets a financially backed production control-plane uptime target of 99.95% or higher for managed Kubernetes. NVIDIA’s 2024 Cloud Services SLA tracks availability every 15 minutes and gives Critical incidents a commercially reasonable 24-hour resolution target. Its Critical definition covers an inoperable or catastrophically failed production service with no workaround, severe performance degradation under reasonable loads, materially incomplete functionality, an inoperable key function or a confirmed material security issue. These figures describe NVIDIA reference terms; they do not guarantee the same terms from an independent neocloud.
Ask the provider to state how a GPU host outage, scheduler outage, storage outage and regional network outage are counted. Clarify whether planned maintenance pauses the clock, whether credits are the sole remedy, and whether repeated breaches allow you to cancel without an early-termination fee.
Inspect operational history and support
Request an incident and status history for the region and service class you will use. During the pilot, open a noncritical support ticket, a performance question and a simulated severity-one escalation. Record acknowledgement time, technical depth, hand-offs, timezone coverage and the quality of the post-incident report. Confirm who can authorize capacity changes and emergency host replacement outside business hours.
Verify security, compliance and software supply chain
Security review should cover the provider’s control plane, hosts, images, network and people, not only encryption at rest. Obtain architecture diagrams and current evidence for the controls that matter to your data and jurisdiction.
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Minimum evidence to request
- Identity integration, role-based access, short-lived credentials, MFA and break-glass procedures.
- Tenant isolation for GPUs, memory, storage, management networks and logs.
- Encryption in transit and at rest, key ownership, rotation and customer-managed-key options.
- Administrative access logging, session recording where applicable, and retention periods.
- Image provenance, registry controls, vulnerability scanning and signed artifacts.
- Driver, firmware and host operating-system patch schedules, emergency fixes and change notices.
- Vulnerability disclosure, incident notification deadlines and evidence preservation.
- Data residency, subprocessors, retention, deletion verification and required certifications or attestations.
NVIDIA AI Enterprise materials describe secure software supply-chain practices, vulnerability mitigation, enterprise support and global security and compliance requirements. Treat those as examples of evidence to request, then verify the controls in the specific neocloud contract and architecture.
Run a practical security test
Deploy a least-privilege identity, attempt an intentionally disallowed cross-tenant access, inspect audit records, rotate a secret, revoke a token and delete a test dataset. Confirm that logs are exportable to your security platform and that the provider can identify administrators who accessed your environment. Ask how a compromised image, driver vulnerability or exposed credential is contained without destroying your only copy of a checkpoint.
Assess APIs, observability and day-two operations
Production suitability depends on what your team can automate and diagnose. Test the documented API and infrastructure-as-code provider, not only a web console. Create, resize, stop, start and destroy the exact resources your deployment pipeline needs. Confirm idempotency, pagination, rate limits, error codes and versioning.
Signals you should be able to export
- GPU utilization, memory use, power, temperature, ECC and Xid or equivalent hardware errors.
- Node health, scheduler queue time, placement decisions, preemption and maintenance events.
- Network throughput, packet loss, RDMA errors and storage latency, IOPS and capacity.
- Job, container, Kubernetes and control-plane audit logs.
- Raw usage records for GPU-seconds, storage, snapshots, network, support and idle reservations.
NVIDIA’s current AI-cloud requirements state that availability, response, performance, metering and telemetry targets should be incorporated into applicable SLAs unless otherwise stated. Require raw records so finance can reconcile the provider invoice with your own workload and so engineering can distinguish an application regression from infrastructure contention.
Calculate total cost at realistic utilization
Compare more than the hourly GPU rate. Build a workload-level model that includes compute, managed control-plane charges, storage capacity and operations, snapshots, data transfer, support, reserved capacity, idle headroom, queueing and migration work.
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| Cost component | Question to answer |
|---|---|
| Compute | What is the price for on-demand, reserved, committed and interruptible capacity, and what minimum term applies? |
| Capacity | Is the quoted rate backed by a reservation, or can the provider place you in a queue during a demand spike? |
| Storage | What do primary data, scratch, snapshots, replicas and deleted-but-retained objects cost? |
| Network | What are ingress, egress, cross-zone and cross-region charges? |
| Operations | Are Kubernetes control-plane, API, support, observability or security features metered separately? |
| Migration | What will it cost to export images, weights, checkpoints, datasets and logs, and to re-create the environment elsewhere? |
Use the utilization your pilot achieved, not a theoretical 100%. A low GPU price can lose its advantage through queueing, poor topology, low utilization, storage charges or forced overprovisioning. Calculate cost per training step, completed job, served token and request at both normal and peak utilization. Model interruption and recovery costs for spot or interruptible capacity, including lost work since the last checkpoint.
Make capacity and regional placement contractual
State the GPU count, node shape, region, start date, ramp schedule and growth ceiling in the order form or reservation. Ask whether capacity is physically dedicated, logically reserved or merely a best-effort quota. Confirm lead time for adding nodes and the provider’s process when the requested shape is unavailable.
Regional placement affects latency, data residency, disaster recovery and network cost. Measure the path to users, databases, object stores and observability systems. For a business-critical service, shortlist at least two providers or two independent regions and document how traffic and checkpoints move during a regional outage.
Prove portability and an exit path
Portability is a test, not a clause. Export container images as OCI artifacts, model weights, checkpoints, datasets, logs, infrastructure definitions and configuration. Document secrets rotation rather than exporting live credentials. Prefer standard Kubernetes, portable object formats and documented APIs where they meet performance requirements.
Perform a restore outside the primary environment
- Export a representative image, model, checkpoint, dataset slice, manifests and configuration.
- Restore them on a second provider or a controlled local environment.
- Recreate identities and secrets with new values; do not copy production credentials.
- Run a smoke test, then resume a job from the checkpoint.
- Measure data-transfer time, missing dependencies, performance difference and engineer hours.
- Record every provider-specific feature that required redesign or manual work.
Do not call the plan portable until a restore produces a valid result and your team knows the time and cost. Include termination assistance, data-retention deadlines, deletion evidence and access to billing and audit records in the contract.
Use a staged procurement gate
A weighted scorecard keeps a cheap but unsuitable offer from winning on one dimension. Set weights according to business impact; latency-sensitive inference should emphasize tail latency and regional network paths, while large-scale training should emphasize interconnect, checkpoint throughput and reservation guarantees.
| Gate | Evidence required | Pass condition |
|---|---|---|
| 1. Architecture and security review | Topology, service diagrams, control evidence, regions, data flows and support model | No unresolved critical isolation, residency or operational gap |
| 2. Small proof of concept | Your container, identity model, API workflow and sample data | Provisioning, deployment, logging and teardown are repeatable |
| 3. Production-like benchmark | Identical scripts, versions, data movement and concurrency across candidates | Meets throughput, latency, scaling and cost thresholds |
| 4. Failure and restore drill | GPU or node failure, storage interruption, checkpoint restore and incident escalation | Recovery time and data-loss objective are met with documented actions |
| 5. Capacity and support pilot | Peak reservation, queue behavior, maintenance notice and support tickets | Capacity arrives when promised and support meets response targets |
| 6. Commercial and exit approval | SLA, credits, termination rights, export test, usage records and price model | Contract reflects measured results and an executable exit plan |
Keep a test log with timestamps, region, node shape, driver and CUDA versions, framework versions, dataset checksum, batch size, concurrency, scripts and raw results. Reproducibility matters because providers change images, firmware, schedulers and capacity pools.
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Contract terms that protect production
- Define availability separately for control plane, compute capacity, storage, network and support.
- Specify measurement intervals, maintenance exclusions, incident severities, response and restoration targets.
- Attach capacity quantities, regions, node shapes, delivery dates and replacement obligations.
- State performance or telemetry commitments where they are material to acceptance.
- Require incident notification, root-cause reports and access to relevant logs.
- Set data residency, deletion, retention and subprocessor obligations.
- Define price changes, renewal, cancellation, refunds, service credits and termination for repeated SLA failure.
- Guarantee export access to data, images, checkpoints, logs, usage records and infrastructure definitions.
- Clarify responsibility for drivers, firmware, host patching, Kubernetes upgrades and emergency maintenance.
Warning signs that should stop or slow a deployment
- The provider will not disclose the exact GPU, topology, driver or virtualization mode.
- Benchmark results omit versions, batch sizes, data location, concurrency or failure assumptions.
- “Dedicated” capacity is not defined, or isolation cannot be tested.
- The SLA covers only a web console while GPU capacity and storage remain best effort.
- There is no status history, escalation path, incident report process or regional support coverage.
- Raw usage data is unavailable, making invoice reconciliation impossible.
- Egress, snapshots, control-plane fees or support charges are undisclosed.
- Export depends on proprietary formats and the provider refuses a restore test.
- Security answers are marketing assertions without architecture, audit evidence or timelines.
Decision rule
Select the provider that meets your measured production thresholds with a written capacity commitment, enforceable service terms, credible security evidence and a tested recovery and exit path. If two candidates pass, compare cost per useful output and operational effort at your expected utilization. If neither passes, change the workload or requirements deliberately; do not compensate for an unresolved platform risk by signing a longer commitment.
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