Glossary · 1 minute read
What Is Edge AI?
Edge AI is running AI models directly on a local device—like a phone, camera, sensor, or machine—instead of sending data to the cloud for processing. It improves latency (responses are instant), privacy (data stays on-device), and reliability (works without connectivity). The trade-off is that devices have limited compute, so models must be smaller or optimized through techniques like quantization and distillation. Edge AI fits use cases needing real-time response, data privacy, or offline operation, while the cloud suits the largest, most capable models.
Edge AI runs models on the device, not in the cloud. Here's what it is, why it helps with latency and privacy, and what you trade for it.
What edge AI is
Edge AI runs AI models directly on a local device—a phone, camera, sensor, or machine—instead of sending data to the cloud for processing.
Why it helps
| Benefit | Why |
|---|---|
| Latency | Instant, no network round-trip |
| Privacy | Data stays on-device |
| Reliability | Works offline |
These make edge AI ideal for real-time, private, or offline use cases—like computer vision on cameras or on-device assistants.
The trade-off
Devices have limited compute and memory, so models must be smaller or optimized—via quantization and distillation, and often small language models. This can trade some capability.
Edge vs cloud
| Edge | Cloud |
|---|---|
| Real-time, private, offline | Largest, most capable models |
| Limited compute | Scalable compute |
See cloud AI vs on-premise AI for the related deployment decision—often a hybrid is best.
Where it fits
Choose edge AI when latency, privacy, or offline operation are essential; use the cloud for the heaviest models. Match deployment to the requirement.
Why FISTA
FISTA Solutions builds AI where it should run—edge for real-time and privacy, cloud for scale—through AI enablement, backed by 150+ projects across 12+ countries.
Deploying AI at the edge? Talk to FISTA.
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01What is edge AI?
Running AI models directly on a local device—phone, camera, sensor, or machine—instead of sending data to the cloud. Processing happens on-device, improving latency, privacy, and offline reliability.
02Why use edge AI instead of the cloud?
For instant response (no network round-trip), privacy (data stays on-device), and reliability (works without connectivity). It suits real-time, private, or offline use cases where sending data to the cloud is too slow or sensitive.
03What's the trade-off with edge AI?
Devices have limited compute and memory, so models must be smaller or optimized via quantization and distillation, sometimes trading some capability. The cloud remains better for the largest, most capable models.
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