Cloud inference platforms often take several seconds to respond to voice or video prompts. In use cases like language tutoring, customer service, or gaming, this latency breaks immersion and utility.
The centralized nature of today’s AI platforms poses risks around control, surveillance, and censorship. Users must trust that their data is handled ethically.
Hosting LLMs at scale in cloud infrastructure becomes economically unsustainable when applied to millions of daily users or devices.
The rise of large language models (LLMs) has fundamentally changed how humans interact with machines. These models can understand and generate human like language, making conversations with AI feel more natural.
From writing and research to coding and customer support, LLMs are now helping people work, learn, and create in smarter, faster ways. They’ve turned machines into collaborators, not just tools. However, the current dominant model, cloud based AI inference, suffers from latency, privacy concerns, and scalability limitations.
iG3 proposes a new paradigm: a decentralized edge computing network powered by thousands of AI enabled devices across the globe, providing instant, private, and cost effective real time AI interaction.
These devices form the first inference layer, while high-power data centers with H100/H200 GPUs serve as the fallback for complex requests. Powered by a tokenized incentive system and DID integration on the peaq network, iG3 offers a robust infrastructure for education, entertainment, and productivity use cases.
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.
Latency
Real-time (<500ms) interaction via local edge inference (STT, TTS, LLMs).
Privacy
AI runs locally on user-owned devices. DID-secured. No data leaves device unless necessary.
Scalability
Distributed compute across Edge devices, offloading to cloud only when required.
Transparency
Every device is verifiable via DID on peaq. Task logs and rewards are transparent.
Over-centralization
Users earn $TOPS based on uptime, task completion, and cluster behavior.
Lack of Incentives
iG3 uses regional reward balancing and DensityBoost to encourage global, fair distribution.