Edge AI

Edge AI is running AI models locally on the end device (phone, browser via WebGPU/WASM, IoT device, local server) instead of sending data to the cloud. Key pluses: privacy (data does not leave the device — important for sensitive information), offline work (without internet), low latency (no network round-trip), reduced cloud inference costs. Honestly about the main trade-off, this is key: only SMALLER, lighter models can run on a device. They are inferior to big cloud models (like GPT-4) in accuracy, 'intelligence' and capabilities — this is the price for locality. For simple/specialized tasks it is enough, for complex reasoning — not. Promising 'GPT-4 power on a phone offline' is dishonest. Honestly about device limits: memory, compute, battery are limited; a heavy model can lag, heat the device and drain the battery. The model must be chosen for the real capabilities of the target devices. Honestly about support across device diversity: different phones/browsers/chips have different performance and support — ensuring stable operation across the whole fleet is harder than in a single cloud. Honestly about the scope: Edge AI is justified when privacy, offline or latency are critical and the task is within a compact model's power. If maximum 'intelligence' is needed and devices are diverse — the cloud may be better. A hybrid scheme is often reasonable (simple on the device, complex in the cloud). Honestly about the effect: it gives privacy/speed/offline, but within compact models' capabilities. Honestly about access: target devices and the task are needed. An important boundary: this is Edge AI; cloud models — the main mode of most AI services; mobile — section 832. Picture this: instead of 'everything to the cloud with latency and privacy in question' — AI on the device where it really fits. The base price starts from 60,000 ₽ (depends on devices and the model).

• Data cannot be sent to the cloud (privacy/requirements).
• AI work offline or with minimal latency is needed.
• Cloud inference is expensive at a large request volume.
• Dependence on the internet and a third-party cloud is undesirable.

• Running AI models on the device (mobile/browser/IoT/local)
• Choosing a compact model for device capabilities
• Privacy (data does not leave the device) and offline work
• An honest assessment: does Edge AI fit or is cloud/hybrid better
• Accounting for limits (memory/battery/hardware; device diversity)
• Honest boundaries (smaller models = lower accuracy/capabilities)
• A possible hybrid scheme (device + cloud)
• Handover and review with you

• AI on the device: privacy, offline, low latency
• Reduced cloud inference costs
• A deliberate choice (Edge vs cloud vs hybrid)
• Honest boundaries (compact models; device limits)

• We assess the task, target devices, privacy/offline requirements
• We pick a model for the devices, implement (or hybrid)
• We test on real devices, honestly set boundaries with you

• Can I get big-model (GPT-4) power right on a phone offline?

  No, and that is honest: only smaller, lighter models fit on a device. They are inferior to big cloud ones in accuracy, 'intelligence' and capabilities — this is the price for locality, privacy and offline. For simple and specialized tasks a compact model is enough; for complex reasoning — not. Promising 'GPT-4 on a phone offline' would be deception.

• Is Edge AI always better than the cloud?

  No, honestly: it has its own trade-off. Edge AI wins when privacy, offline or low latency are critical and the task is within a compact model's power. But if maximum 'intelligence' is needed and devices are diverse (different power/support) — the cloud may be better. A hybrid scheme is often optimal: simple on the device, complex in the cloud. We will honestly pick for your task.

• Run a model on the device — and it is all stable?

  Not automatically, honestly: devices differ in memory, power, battery and support (phones, browsers, chips). A heavy model can lag, heat and drain the battery, and ensuring stability across the whole fleet is harder than in a single cloud. We pick a model for the real capabilities of the target devices and test on them rather than promise 'works everywhere the same'.