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Compare GPU cloud providers against the exact workload, location, and data route you need—not a headline uptime percentage or bandwidth figure. First verify whether the specific GPU is covered by the provider’s availability terms, then separate GPU interconnect from VM networking and price outbound transfer by destination and path. Finally, validate the candidates with matched workload tests.
Start with a like-for-like comparison
Choose the workload and deployment assumptions before comparing providers. A useful comparison holds constant the GPU model and count, region, network route, storage arrangement, reservation or interruption model, outbound data volume, and contract terms. If one candidate uses a different topology or transfer destination, record the difference rather than treating its numbers as directly comparable.
For each candidate, collect the exact product and configuration from current provider documentation. Record the page date: GPU offerings, availability terms, network limits, and billing can change. A provider-wide statement is not enough to establish the terms for a particular GPU SKU in a particular location.
Check whether the exact GPU deployment has an availability commitment
Do not assume a general virtual-machine SLA applies to every accelerator configuration. For each GPU instance, confirm its general-availability status, region and zone coverage, the applicable SLA, how availability is measured, exclusions, capacity provisions, claim procedure, and remedy. Keep service uptime separate from the chance of obtaining the GPU capacity you need when you need it: an uptime target does not promise that a specific GPU is available to provision unless the contract says so.
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Google Cloud provides a concrete example of why the SKU and location matter. Its Compute Engine SLA covers an attached GPU instance only when the GPU model is generally available; in a region with multiple zones, the model must also be available in more than one zone. Check the current conditions in Google Cloud’s GPU instance availability documentation rather than applying a VM-level SLA to a single-zone or otherwise ineligible GPU deployment.
For the SLA itself, note the measurement period and what counts as downtime, along with maintenance exclusions, any required configuration, the deadline and evidence for a claim, and whether the remedy is service credits or something else. Do not convert a service-credit remedy into a promise of replacement capacity.
Compare networking by segment, not by one bandwidth number
“Network bandwidth” can refer to different parts of the system. Record the GPU-to-GPU interconnect inside a server separately from the inter-node fabric used by distributed jobs. Then identify the VM’s outbound limit, any per-flow ceiling, aggregate instance or project limits, and the routes to storage, other cloud services, private connectivity, and the public internet.
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Also record the assumptions behind each published figure: GPU machine type, NIC configuration, software or topology, destination, and whether the value is a maximum. A configuration capability is not a guaranteed rate for an application end to end.
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Google Cloud’s 2026 GPU machine documentation, consulted October 7, 2026, lists maximum network bandwidth of 25 Gbps for a3-highgpu-1g and 1,000 Gbps for a3-highgpu-8g. Those are published maxima for those specific configurations, not independently measured application throughput. Google says the maximum cannot exceed the listed figure and that actual egress depends on the destination and other factors. See the GPU machine types documentation.
Follow the traffic to its destination
Google Cloud documents instance and project-level limits and per-flow limits for some outbound paths. A machine’s advertised maximum therefore does not establish what one connection, an internet destination, or a particular application will achieve. Its documentation states: “Bandwidth from the internet is not covered by any SLA and is subject to network conditions.” This is Google Cloud’s Compute Engine network bandwidth documentation, consulted October 7, 2026; it is not a general statement about every provider. Review the applicable Compute Engine network bandwidth limits.
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For distributed training, account for the inter-node topology and traffic between nodes; for data export, account for the route from the VM to the actual destination. A fast intra-node GPU interconnect does not describe either of those paths. Lambda’s On-Demand Cloud overview describes GPU-backed virtual machines and listed families including B200, GH200, and H100, and notes improved bandwidth between GPUs within an SXM physical server. That describes within-server communication; it does not by itself establish a comparable SLA or egress price.
Price egress for the route and volume you will actually use
Estimate outbound data by destination and transfer path, then apply the billing rules for the specific service. Separate internet egress, same-provider or same-region transfer, cross-region transfer, private interconnect, and movement over a third-party fabric. Check the billing unit, directionality, included quotas, rate tiers, and product-specific exclusions. Add fixed costs such as ports, attachments, cross-connects, and facility charges where they apply; a lower per-byte rate can coexist with recurring connectivity costs.
CoreWeave’s pricing page, consulted October 7, 2026, lists egress and input/output operations as free in the displayed pricing sections and lists data transfer within CoreWeave as free. The page separately lists public IP and Direct Connect charges, so those free transfer entries alone do not establish that every network-related cost is zero. Verify the applicable service and terms on CoreWeave Cloud Pricing.
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Google Cloud’s architecture guidance says transfer over Partner or Dedicated Interconnect is charged at a lower rate than internet traffic, while the interconnect can add monthly port or attachment charges; third-party facilities and equipment may add costs too. It also distinguishes connectivity-path availability from GPU compute availability: redundant Dedicated Interconnect topologies have monthly SLAs that vary by topology, while a single connection has no SLA. See Google Cloud’s guidance on connecting other cloud service providers for the path and topology terms.
Use a comparison worksheet with matched assumptions
Fill one row per provider and exact GPU configuration. Use the same workload assumptions across rows, and write “not stated” with the source when a provider does not publish a comparable value; do not infer one from a general product description.
| Axis | Record | Why it matters |
|---|---|---|
| GPU capacity | Exact SKU, GPU model and count, memory, region, zones, reservation or queue terms | Availability and SLA coverage can differ by GPU configuration and location. |
| Availability | SLA scope and target, measurement, exclusions, capacity commitment, claim process and remedy | A headline percentage alone does not establish that usable GPU capacity will be available. |
| GPU networking | Within-node interconnect and inter-node fabric or topology | Distributed training can be limited by communication between GPUs or hosts. |
| Egress limits | VM maximum, per-flow ceiling, aggregate quota, route and destination | The effective limit depends on both the path and traffic pattern. |
| Transfer cost | Outbound volume by destination, included amounts, rate tiers and billing unit | Data-heavy workloads can have materially different transfer costs. |
| Connectivity cost | Ports, attachments, private interconnect, fabric, cross-connect and facility charges | Private paths can reduce per-byte transfer charges while adding fixed costs. |
| Validation | Benchmark, traffic shape, destination, region, software, measurement window and date | Matched tests make documentation-based candidates more useful to compare. |
Validate the comparison with representative tests
Use at least one benchmark that reflects the intended training or inference traffic and one scenario that exports data to the intended destination. Keep the GPU count and model, region, route, software, traffic shape, and measurement window aligned as closely as possible. For each run, capture throughput, latency, packet loss or retries where applicable, time to provision, and billed transfer.
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Report these results as your team’s measurements, with the configuration and test date. Published maxima, provider SLAs, and your observed benchmark results answer different questions; do not present a test result as a provider guarantee.
What the available provider examples do—and do not—show
The cited documentation offers useful examples, but it does not normalize SLA scope, capacity guarantees, network performance, egress and interconnect prices, and geography across providers. The Google Cloud configuration figures are tied to specific machine types; the CoreWeave pricing entries are tied to the displayed pricing sections; Lambda’s SXM note concerns within-server GPU bandwidth. These details help identify what to check, but they are not enough to rank providers across workloads or regions.
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