Edge vs Cloud GPUs for Inference: When to Run Models Locally and When to Use a GPU Cloud

Inference is where AI becomes real, the moment a model makes a decision that users can feel. “Fast enough” depends on more than GPU speed. Distance, bandwidth, reliability and data rules all show up in your latency and your bill. The practical question is not “edge or cloud.”

It is “which requests should run locally and which should go to a GPU cloud,” and how to route between them cleanly.

Edge GPUs can now run serious pipelines. NVIDIA’s Jetson AGX Orin, for example, is marketed at up to 275 TOPS of AI performance in an embedded module form factor. On the other side, networks are pushing GPUs closer to users. Cloudflare has described deploying GPUs across more than 180 cities for inference. If you are designing the “where should this run” layer end to end, it helps to anchor your thinking in practical edge computing patterns and deployment models.

Factors Determining Inference Placement

Here are some of the key factors you should consider when determining where to run inference workloads.

1. Latency is often a network problem

If your application feels “instant,” it is usually because your slowest 5 percent of requests are still fast. Network distance and congestion show up most harshly in p95 latency.

A measurement study comparing cloud data centers and edge servers found that 58% of end users could reach a nearby edge server in under 10 ms, while only 29% could achieve similar latency to a nearby cloud location. The same paper also reports that for a significant majority of users; edge servers are closer than cloud providers by 10 to 100 ms.

For modern generative systems, hybrid methods can cut tail latency. The recent Splitwise paper reports 53 to 61% lower 95th percentile latency versus cloud only execution in its experiments, by adaptively partitioning inference between edge and cloud.

2. Bandwidth and egress costs can dwarf compute costs

Cloud inference is simplest when the input is small, like text prompts or embeddings. It becomes expensive and unstable when you must ship raw video or high-rate telemetry.

Cloud pricing makes that penalty explicit.

• AWS lists tiered data transfer out pricing, including $0.09 per GB for the first 10 TB per month before volume discounts.
• Azure’s bandwidth pricing similarly charges tiered per GB rates after a free allowance, with rates that vary by source region.

If you are sending many gigabytes per day, bandwidth can rival GPU spend and it adds jitter that hurts tail latency. A common pattern is to move less data, not just more compute. Do filtering, detection, compression or embedding at the edge, then send compact results upstream.

3. Compliance and geography are becoming design constraints

Where inference happens can become an audit question. The EU Data Act applies from Sep 12, 2025 and its cloud switching rules push providers toward lowering and eventually removing certain switching charges on timelines that extend into 2027.

Physical cloud GPU availability is also uneven. If your nearest accelerator-enabled region is far away, edge inference can be the difference between a smooth product and a laggy one.

4. Cost is aboututilization and risk, not just hourly rates

Cloud GPUs win on elasticity. You can scale up forup spikes, then scale them down. You can also trade interruption risks for savings. AWS says Spot Instances can be discounted by up to 90% versus On Demand pricing.

Edge GPUs win when load is steady and local, especially when they avoid recurring transfer fees and keep sensitive data on site.

Cloud reduces fleet management, but ongoing spend is easy to misjudge. Flexera’s 2025 State of the Cloud release says 84% of organizations cite managing cloud spend as the top cloud challenge.

5. Energy is now part of the conversation

The International Energy Agency estimates data centers consumed around 415 TWh of electricity in 2024, about 1.5% of global electricity use and projects demand could more than double toward 2030 in its base case.

6. Reliability and offline tolerance

If your product must work in a factory, a vehicle, a hospital wing or a retail store with imperfect connectivity, edge inference is not an optimization. It is a requirement.

Even when the cloud is available, local execution can provide graceful degradation. So, keep basic safety and core UX local and use cloud for enhanced reasoning when the network is healthy.

Edge vs Cloud GPUs: Simple Decision Rule

Use edge GPUs when the product is defined by immediacy, locality or confidentiality. Use cloud GPUs when it is defined by scale, experimentation or rapid model churn.

Run inference locally when:

• You need tight and consistent latency and network RTT would dominate.
• Your inputs are large or frequent and transfer cost or jitter is unacceptable.
• You must keep data on site or within a jurisdiction.
• You must operate during outages.

Use a GPU cloud when:

• You need a large model, fast iteration or the latest accelerators.
• Demand is spiky or unknown.
• Inputs are small enough that transfer stays a minor line item.
• You can exploit cloud levers like Spot capacity to reduce unit cost.

Note: A good starting point is to set a latency SLO and a cost envelope, then benchmark the same model on one edge node and one cloud endpoint. Compare p95 latency and full costs, not just GPU hours.

Hybrid Patterns that Beat Single Location Designs

Most teams end up with routing, not a permanent choice:

1. Edge preprocessing, cloud reasoning

Do early stages locally, then send compact representations to the cloud for heavier reasoning.

2. Model cascading

Run a small, fast model locally for most requests. Escalate only uncertain or high value cases to a larger cloud model.

If you need cloud operations but cannot afford long RTTs, consider metro-edge GPU networks where providers place accelerators close to users.

Wrapping Up!

Edge and cloud GPUs are converging, but they still win for different reasons. Edge wins when milliseconds, bandwidth, privacy and offline operation are core requirements. Cloud wins when you need elastic scale, rapid iteration and access to top tier hardware without buying it.

Therefore, you should decide with measurements, not ideology, while measuring end-to-end latency. This includes the network and cost per successful inference at your target quality, including data transfer.

When the network adds a large and unpredictable chunk, keep inference close to the data. When demand swings or models change weekly, push more work to the cloud. And when reality sits in the middle, build a routing layer that lets you use both on purpose.