GitHub - aslankose/imece: A decentralized AI compute cooperative where contributors earn inference credits by donating idle GPU/CPU time — measured in FLOPs, not crypto. · GitHub
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imece
A decentralized AI compute cooperative where contributors earn inference credits by donating idle GPU/CPU time — measured in FLOPs, not crypto.
imece is an open-source framework that allows anyone to contribute idle compute resources in exchange for AI inference credits — denominated in floating-point operations (FLOPs), not cryptocurrency.
The core idea: You donate idle GPU/CPU time → you earn GigaFLOP-Tokens (GFT) → you spend GFT to access AI inference. No speculation. No financial value. Just compute for compute.
Motivation
AI inference is increasingly powerful but increasingly centralized. Access is gated by capital, not contribution. Meanwhile, millions of GPUs sit idle every night across the world, in different time zones, on different grids.
imece turns that idle capacity into a global cooperative — one where the communities that bear the cost of AI infrastructure are also empowered to benefit from it.
A secondary benefit: because contributor nodes are globally distributed across time zones, computation naturally migrates toward regions with low electricity demand and high renewable availability at any given hour — a passive energy-efficiency property that centralized data centers cannot replicate.
How It Works
Contribute idle compute → earn GFT tokens → spend tokens on AI inference
Token formula:
T_earned = FLOPs_delivered × Hardware_Multiplier × Reliability_Factor
Token cost per inference:
T_spent = FLOPs_per_model × Output_tokens × Precision_factor
All tokens are:
Denominated in GigaFLOPs (objective, hardware-agnostic)
Non-transferable and non-tradeable by design
Tied to the wallet that earned them
Architecture
The framework has four components:
Component<br>Role
Contributor Client<br>Benchmarks device, serves transformer model layers, manages token wallet
Coordination Layer<br>Dispatches tasks, assigns hardware multipliers, issues tokens, routes inference
Token Ledger<br>Append-only hash-chained log of all GFT issuance and redemption
Inference Cluster<br>Custom distributed pipeline (volunteer nodes) primary, centralized fallback
Distributed Inference Architecture
imece implements a custom layer-sharding system for distributed inference:
Primary: Volunteer contributor nodes each serve a contiguous slice of transformer layers. Inference requests flow through the pipeline — activations pass from node to node until the final output is generated. Contributors earn tokens proportional to FLOPs delivered. Any HuggingFace-compatible transformer model can be served — LLaMA 3, Mistral, Mixtral, and others.
Fallback: A centralized inference service — used when the volunteer pipeline is unavailable, ensuring reliable access at all times. The current implementation includes a Groq fallback path, with additional providers welcome as community contributions.
This makes the token economy architecturally honest — earned tokens are backed by compute that directly contributes to real AI inference.
Hardware Multiplier Tiers
Hardware Class<br>Example Devices<br>Multiplier
Mobile / Edge<br>Smartphone SoCs, Raspberry Pi<br>0.05×
CPU Only<br>Desktop / server CPUs<br>0.10×
Entry Consumer GPU (integrated)<br>Intel UHD, AMD Radeon integrated<br>0.50×
Mid Consumer GPU (baseline)<br>RTX 3060, RX 6700 XT<br>1.00×
High Consumer GPU<br>RTX 4080, RX 7900 XTX<br>2.00×
Prosumer GPU<br>RTX 4090, RTX 6000 Ada<br>3.00×
Professional Accelerator<br>A40, L40S<br>5.00×
Data Center Accelerator<br>A100,...