The right GPU cloud purchase is not simply the lowest GPU-hour price. First size the workload, then verify the node topology and regional capacity, model the full cost of completed work, and negotiate protection for both outages and capacity shortfalls. On-demand instances suit uncertain experiments, spot capacity can cut prices for checkpointed jobs, and reserved or private clusters make sense when production demand is predictable. A usable contract must define what happens when the service is reachable but the GPUs you bought are unavailable.
Start with the workload, not the provider
Write down the workload’s constraints before comparing clouds. A training run that fits in memory but requires frequent GPU-to-GPU synchronization may perform worse on a loosely connected cluster than on a smaller node with a faster interconnect. An inference service may need fewer GPUs but stricter latency and capacity guarantees.
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Record the inputs that determine capacity
- Model memory: Include weights, optimizer states, gradients, activation memory and framework overhead. State whether quantization, sharding or offload is part of the design.
- Batch size and sequence length: These often change memory use and throughput more than the parameter count alone.
- Target throughput or latency: Specify training steps per second, tokens per second, rendered images, or an inference-response target.
- Parallelism strategy: Note whether the job uses data, tensor, pipeline or expert parallelism, and how much communication occurs between GPUs.
- Checkpoint interval and restart time: These determine whether an interrupted job can resume cheaply enough to use spot capacity.
- Storage footprint: Count datasets, checkpoints, logs, container layers and temporary files.
- Data-egress pattern: Record how often results leave the region or provider and how large those transfers are.
Turn these measurements into a capacity test: run a representative slice on the candidate node, measure throughput and memory headroom, then calculate the number of GPUs needed to meet the deadline. Do not infer performance from aggregate VRAM alone.
Estimate GPU count from a deadline
- Measure the work completed per GPU or per node under the intended batch size and sequence length.
- Multiply that rate by the planned GPU count and apply the measured scaling efficiency for your parallelism method.
- Add checkpoint, evaluation and data-loading time to obtain an end-to-end rate.
- Divide the total work by that rate and compare the result with the deadline.
- Repeat the calculation with a failure or pre-emption allowance if the job cannot slip.
The result is a workload-specific GPU count, not a universal ratio between model size and GPUs. Keep separate counts for a fast training cluster, a lower-latency inference tier and a recovery environment; their optimal topologies may differ.
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Match GPU memory and topology
Compare each offer at the node level. A node with enough aggregate memory can still underperform when the workload needs high-bandwidth communication between GPUs.
GPU memory and generation
Memory per GPU sets the largest shard, batch or context that can reside without offload. Newer GPU generations may improve throughput, but a cheaper older generation can win when the workload is memory-bound and the node supplies enough capacity. Validate with your own software stack, drivers and precision settings.
GPUs per node and interconnect
Ask how many GPUs share a node, what interconnect links them, and what bandwidth is available for collective operations. Request a topology diagram or an equivalent specification for multi-GPU jobs. Also compare host CPU and RAM, local scratch capacity and network bandwidth; data preparation or checkpointing can bottleneck a fast accelerator.
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Confirm whether local scratch is ephemeral, how persistent volumes are attached, and where object storage sits relative to the GPUs. Price and test the path used for dataset reads, checkpoint writes and result egress. A low compute rate can be offset by storage and transfer delays if the job repeatedly moves large files across regions.
Choose on-demand, spot or reserved capacity
| Commitment model | Best fit | Advantages | Risks to price and availability |
|---|---|---|---|
| On-demand | Experiments, bursty work and uncertain demand | No long-term commitment; start and stop as needed | Hourly price can be higher and the required GPU may be unavailable when demand peaks |
| Spot | Checkpointed training, batch rendering and other interruptible jobs | Lower published rates where spare capacity exists | Pre-emption, variable availability and restart overhead; savings disappear if interruptions waste work |
| Reserved or private cluster | Predictable production utilization and guaranteed access requirements | Capacity planning, dedicated hardware options and a clearer supply commitment | Take-or-pay exposure, long terms, idle capacity and possible limits on moving the reservation between regions or GPU types |
Questions to ask before signing a reservation
- Is the reservation take-or-pay, and are unused hours forfeited?
- Can capacity move between regions, clusters or GPU generations?
- What happens if the contracted SKU is unavailable: delayed delivery, a substitute GPU, or a credit?
- Is hardware physically isolated, and how quickly are failed nodes replaced?
- Does the commitment cover storage, networking and support, or only accelerator time?
- Can you reduce, expand or terminate the term if your model or demand changes?
Use a mixed strategy when appropriate: reserve the baseline needed for a launch or service-level objective, use on-demand for bursts, and send restartable experiments to spot pools. Keep the scheduling and checkpoint logic identical across all three so a job can move when availability changes.
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Published capacity and pricing examples
The following figures are list prices or reservation structures published by the named providers. They are not quotes for a particular region or customer. Taxes, storage, networking and negotiated terms can change the effective rate.
| Provider and system | GPU configuration | On-demand | Spot | Qualification |
|---|---|---|---|---|
| CoreWeave NVIDIA HGX H100 | Eight GPUs; 80 GB VRAM per GPU | $49.24 per hour | $19.71 per hour | Published list prices observed 2026-10-18; region, taxes, storage and networking may differ |
| CoreWeave NVIDIA HGX H200 | Eight GPUs; 141 GB VRAM per GPU | $50.44 per hour | $20.93 per hour | Published list prices observed 2026-10-18; region, taxes, storage and networking may differ |
| Lambda Private Cloud | Single-tenant clusters of 1,000+ NVIDIA HGX B200 GPUs | Not stated | Not stated | Reservations described as one to three years; no public hourly price in the cited documentation |
| Lambda hyperscale private cloud | 2,000–64,000 NVIDIA GPUs | Not stated | Not stated | Reservations described as three to ten years; no public hourly price in the cited documentation |
Regional inventory, queue times, negotiated enterprise rates, outage history and provider-specific failover performance are not fully published in the cited pages. Request a current quote and a written capacity commitment for production deployments.
Calculate effective cost per completed work
GPU-hour is only the first line of the bill. Build a monthly model that includes:
- Accelerator time, including warm-up and idle periods.
- Persistent storage, snapshots and checkpoint retention.
- Inter-region traffic and data egress.
- Orchestration, control-plane and managed-service fees.
- Support and software licenses.
- Unused hours on a reservation.
- Migration, image-building and engineering work required to operate a second provider.
- The expected cost of interruption, including recomputation and missed deadlines.
Compare providers using the unit that matters to your business: cost per completed training step, rendered image, tokens served or inference request. For a training run, an illustrative calculation is:
effective cost per step = (compute + storage + network + support + interruption cost) ÷ completed steps
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Use measured throughput and restart behavior rather than assuming a spot discount is pure savings. A 60% lower hourly rate can be uneconomic if frequent pre-emption causes 40% of the work to be repeated. Conversely, a higher-priced node may win if its memory and interconnect allow a smaller, faster cluster.
Understand what a GPU-cloud SLA actually covers
Separate service availability from capacity availability. A control plane can be reachable while the GPU type, region or quantity you reserved cannot be allocated. Those are different failures and should have different measurements and remedies.
NVIDIA DGX Cloud targets
NVIDIA’s Cloud Services SLA, last modified November 5, 2025, sets DGX Cloud Accelerate Computing Environment (ACE) targets of 99% service availability and 95% capacity availability per calendar month. Service availability is measured every 15 minutes; capacity availability is measured every 60 minutes. Gaps shorter than those intervals are excluded.
“NVIDIA DGX Cloud will be delivered with the following target Service Availability and Capacity Availability levels per each Accelerate Computing Environment (ACE) measured over a calendar month: Service Availability: 99%; Capacity Availability: 95%.”
To claim a validated failure, the customer must submit the claim within two months and include incident timing, the affected offering, users and troubleshooting details. The remedy is a credit equal to the interruption scope, usable only toward a future DGX Cloud order term. That is not the same as a cash payment or compensation for downstream business loss.
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Contract terms to compare across providers
- Definition: Does “capacity” mean any GPU, the contracted SKU, a region, a node count or a complete cluster?
- Measurement window: Calendar month, rolling period or per incident?
- Minimum outage: Are short interruptions or queue delays excluded?
- Maintenance and force majeure: Which events are excluded, and how much notice is required?
- Customer exclusions: Are configuration errors, quota exhaustion or software faults removed from the calculation?
- Evidence and deadline: What logs, timestamps and ticket numbers must accompany a claim, and when is it due?
- Remedy: Cash refund, future-use credit, service-term extension or another remedy?
- Scope: Does the SLA cover storage, networking, support response and replacement hardware, or only the compute service?
Negotiate capacity shortfall protection separately from software-service uptime. A credit that starts only after a long outage may not help when a launch depends on a particular GPU being available at a particular time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect a training run from an outage
1. Make checkpoints portable
Write checkpoints to provider-independent object storage when practical, and keep enough history to recover from a corrupt or incomplete write. Test restoring a checkpoint on a clean environment; record both restore time and the exact driver, framework and container versions required.
2. Keep a tested second location
For a critical job, maintain a second provider or region with reproducible images, drivers, infrastructure-as-code and access credentials. “We can move” is not a recovery plan unless the alternate environment has been started and a checkpoint has successfully resumed there.
3. Automate interruption handling
Have the job trap termination notices, flush a checkpoint, record the last completed step and exit cleanly. On restart, verify checkpoint integrity, avoid duplicate output and resume from the recorded step. Apply this logic to spot and on-demand instances alike.
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4. Define acceptable substitutes
Before signing, state whether a failed H100 allocation may be replaced by another GPU generation, a different memory size or a different region. Test the substitute against memory, precision, performance and licensing requirements; otherwise a nominal replacement may be unusable.
5. Measure recovery objectives
Set a recovery point objective (how much work may be lost) and a recovery time objective (how quickly work must resume). Include time to obtain capacity, attach storage, pull images, restore checkpoints and warm the distributed job. Use those measurements to decide how much reserved capacity and redundancy to buy.
Licensing, support and data controls
Virtual GPU software licensing
NVIDIA states that virtual GPU software can be purchased per concurrent user or per GPU as an annual subscription or perpetual license. A current SUMs contract provides support, upgrade and maintenance access. Determine whether the license follows the user, the GPU, the host or the cluster, and verify that the terms permit failover to your second location.
Support response
Ask for response and replacement targets by severity, escalation contacts and the boundary between hardware, network and software support. A 24×7 hardware-support promise is useful only if it specifies how failed nodes are diagnosed, isolated and returned to service.
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Identify the physical region for GPUs, persistent storage, snapshots, logs and support access. Confirm retention and deletion procedures, encryption responsibilities and whether a failover region changes the data-residency position. Include egress approval and transfer time in the recovery plan.
A practical provider-evaluation checklist
- Send each provider the same workload profile: model memory, batch and sequence length, target throughput or latency, parallelism, storage and egress.
- Request the exact GPU SKU, memory per GPU, GPUs per node, interconnect, host CPU and RAM, local scratch and network bandwidth.
- Obtain current regional inventory, expected queue time and a written answer on how reserved capacity is protected when the SKU is short.
- Price on-demand, spot and reservation options using the same workload unit, including idle, storage, transfer, support and interruption costs.
- Read the SLA definitions and exclusions; insist on separate service and capacity measurements.
- Confirm claim deadlines, required evidence and whether the remedy is cash, credit or term extension.
- Run a representative benchmark and a checkpoint-restore test in the primary and alternate locations.
- Document portability: images, drivers, infrastructure code, licenses, data paths and approved substitute GPUs.
- Review the exit terms, data export process and the cost of moving to another provider or region.
Bottom line
Buy GPU cloud capacity as an end-to-end production system. Size it from measured workload behavior, select a topology that matches communication and memory needs, and compare cost per completed output rather than headline hourly rates. Use on-demand for uncertainty, spot for interruption-tolerant work and reservations or private clusters for predictable utilization. Finally, treat an SLA as a narrowly defined remedy—not insurance: portable checkpoints, a tested alternate location and explicit capacity-shortfall terms are what keep a failed GPU allocation from becoming a missed deadline.
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