GEIANT Hive
Community-powered distributed AI inference. H3-coherent. GNS-settled.
GEIANT Hive is Layer 4 of the GEIANT stack. It turns idle community devices — laptops, desktops, phones — into a geographically-coherent inference network. Workers register their hardware in an H3 cell, claim inference jobs atomically from a Postgres queue, execute model shards locally, and receive GNS tokens on Stellar mainnet.
How it fits in the stack
Layer 4 — GEIANT Hive ← this section
Community swarm, job queue, GNS settlement
Layer 3 — Perception Runtime
Satellite tiles, weather, embeddings
Layer 2 — Agent Identity Protocol
Ed25519 identity, delegation certs, breadcrumbs
Layer 1 — GEP (Geographic Addressing)
569 trillion addressable points
Layer 0 — H3 Hexagonal Grid
Geospatial indexing foundation
Hive inherits all four layers below it. A Hive worker has an Ed25519 identity (L2), is located in an H3 cell (L0/L1), and settles payments on Stellar using the same keypair (L2). The compliance report for a Hive inference session is a L2 breadcrumb chain.
Key concepts
Geospatial Pod — A virtual inference endpoint assembled from idle devices within a specific H3 cell cluster. The customer hits an OpenAI-compatible API endpoint; the swarm is transparent.
H3 coherence — The scheduler routes jobs to devices in the same H3 cell. At Resolution 7 (~5 km²), inter-shard network latency is below 3ms — low enough for pipeline-parallel transformer inference. See H3 Resolution Reference.
Atomic claiming — Workers poll a Postgres hive_jobs table every 5 seconds. Job claiming uses FOR UPDATE SKIP LOCKED — a Postgres pattern that guarantees exactly-once assignment with no application-level locking and no race conditions, even under high concurrency.
GNS settlement — On job completion, GNS tokens are distributed on Stellar mainnet via a 60/25/10/5 split. The worker earns 60% of the job's gns_reward. Settlement is async; the worker's tokens_earned balance is updated immediately via an atomic Postgres increment.
Revenue split
Every inference job distributes GNS automatically:
| Slice | Recipient | Purpose |
|---|---|---|
| 60% | Community node operators | Weighted by trust tier and thermal stability |
| 25% | GEIANT / ULISSY s.r.l. | Orchestration, scheduler, API gateway |
| 10% | Hydration & Resilience Fund | Seeding bonuses for new H3 cells |
| 5% | Sovereign Quorum | BFT coordinator fee |
No tokens are burned. All GNS stays in circulation.
Three access patterns
| Pattern | Who | Entry point |
|---|---|---|
| Consumer | GCRUMBS app users | Send a message to @hai |
| Developer | Any app or script | POST /v1/chat/completions |
| Enterprise | Organisations with private clusters | MDM-deployed workers + API key |
What Hive is not
- Not a training platform. Training requires backward passes and gradient synchronization across all parameters simultaneously — a use case where dedicated A100s win decisively. Hive is inference only.
- Not suitable for sub-100ms first-token requirements. Pipeline fill (the first token) takes one full pass through all devices. At Res-7 with 4 devices this is ~180ms. Subsequent tokens flow at the pipeline's steady-state rate.
- Not a replacement for 70B+ models on a single device. Very large models require 30+ devices in one cell. At current network density this is only viable in dense Res-5 (city-scale) swarms.