Running AI models on-device offers latency and privacy benefits, while cloud AI provides superior capability. We compare both architectures and their optimal use cases.
The question of where AI inference should run, on local devices at the edge or in centralised cloud data centres, is one of the most consequential architectural decisions facing technology builders today. Each approach has fundamental trade-offs in latency, privacy, capability, and cost. The answer is not one or the other but a carefully designed combination that matches the right architecture to each specific use case.
The Case for Edge AI
Edge AI, running models directly on user devices or local servers, offers three compelling advantages. First, latency: local inference eliminates the round-trip time to cloud servers, enabling real-time applications like autonomous vehicles, industrial robotics, and augmented reality that cannot tolerate network delays. Second, privacy: data never leaves the device, which is critical for healthcare, financial, and personal applications where data sovereignty matters. Third, reliability: edge AI works without network connectivity, important for industrial and field applications. Apple's on-device intelligence, Qualcomm's AI Engine, and NVIDIA's Jetson platform are driving rapid improvement in edge AI capability.
The Case for Cloud AI
Cloud AI offers access to vastly more powerful models than any edge device can run. Frontier language models with hundreds of billions of parameters require GPU clusters that cannot fit in a phone or even a powerful laptop. Cloud infrastructure also allows instant scaling: serving one user or one million users requires no changes to the application architecture. For applications requiring the most capable AI models, such as complex document analysis, code generation, or sophisticated reasoning, cloud deployment remains the only viable option.
The Hybrid Architecture
The most practical approach for most applications is a hybrid architecture that runs simple, latency-sensitive tasks at the edge and routes complex tasks to the cloud. A mobile application might use an on-device model for real-time speech recognition and basic intent classification, then send complex queries to a cloud-hosted model for detailed responses. At QverLabs, our sports vision platform uses this approach: lightweight detection models run on edge hardware for real-time ball tracking, while more complex trajectory analysis and commentary generation happen in the cloud.
The Convergence Ahead
The boundary between edge and cloud AI is shifting as hardware improves. Each generation of mobile and edge processors can run larger models, gradually extending the range of tasks feasible at the edge. Techniques like model distillation, quantisation, and pruning make powerful models more compact. Within five years, devices may run models locally that today require cloud infrastructure. Designing applications with this convergence in mind, abstracting the inference location from the application logic, will provide the flexibility to migrate workloads as hardware capabilities evolve.
