# iG3 Solution

As AI engineers and robotics enthusiasts, we encountered the same pain points that many developers, creators, and innovators face daily:

* Delays when speaking to AI assistants.
* Robots struggling to respond in real-time.
* Cloud models breaking immersion due to latency.
* High infrastructure costs to deliver low-latency AI at scale.

We've built robots that needed to understand humans but by the time the cloud responded, the moment was already lost. We've built prototypes that worked brilliantly in the lab but crumbled in the real world because latency made them feel dumb.

That's why we built **iG3,** not as a product, but as a solution to our own frustration.

We imagined a network where intelligence lived **close to the user**, not halfway around the world. Where devices could:

* Listen, think, and speak in real time.
* Run LLMs, vision models, and voice pipelines without relying on cloud APIs.
* Work together in a secure, decentralized mesh.
* Be owned by the people who run them and reward those who do.

We designed a hybrid system:

* **M1 Devices** act as intelligent gateways — capturing inputs, managing sessions, verifying identities.
* **M1 Mini, M1E** specialize in inference — running vision, voice, and multimodal models at the edge.
* **LLM Gateways** provide fallback for complex tasks — tapping into powerful H100/H200 GPU clusters.

It’s a symphony of AI at the edge — fast, modular, and human-centric.

We didn’t stop at performance. We built the incentives to scale:

* A token system that rewards real contribution.
* A regional mining mechanism that encourages global participation.
* DID integration to anchor trust and verifiability.

This is more than infrastructure. This is a movement.

**iG3 is for the builders. The tinkerers. The believers in better.**

If you’ve ever shouted “Hey AI!” and waited too long for a response, you’ll understand why iG3 needs to exist.

### How iG3 Solves Problems

<table><thead><tr><th>Problem</th><th width="271.63671875">Solution</th></tr></thead><tbody><tr><td>Latency</td><td>Real-time (&#x3C;500ms) interaction via local edge inference (STT, TTS, LLMs).</td></tr><tr><td>Privacy</td><td>AI runs locally on user-owned devices. DID-secured. No data leaves device unless necessary.</td></tr><tr><td>Scalability</td><td>Distributed compute across Edge devices, offloading to cloud only when required.</td></tr><tr><td>Transparency</td><td>Every device is verifiable via DID on peaq. Task logs and rewards are transparent.</td></tr><tr><td>Over-centralization</td><td>Users earn <a href="/pages/vHXIBGZhK0YWWcoOwWLC">$TOPS</a> based on uptime, task completion, and cluster behavior. </td></tr><tr><td>Lack of Incentives</td><td>iG3 uses regional reward balancing and DensityBoost to encourage global, fair distribution.</td></tr></tbody></table>


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