EDGE_AI_INFERENCE_ROUTING
Find the nearest GPU with the right model loaded — without a central scheduler
AI inference at the edge requires finding a nearby GPU with the requested model loaded, with capacity now, satisfying latency and privacy constraints. Centralized routing (Together AI, Replicate, frontier APIs) is fast but ships data to remote regions. Hyperweave turns 'tier 4-6 + within 50 km + model X loaded' into a single peer-owned query — community GPUs become reachable capacity that no provider sees.
Global Inference Network
Real-time visualization of LLM queries routing through Hyperweave's geo-distributed compute mesh
INFERENCE_NODE
GPUQPS
12.4K
TOKENS
847K
CACHE
94%
NETWORK_LOG
COMPUTE_NODES
DATA_FLOW
MULTI_DIMENSIONAL_DISCOVERY
One query combines a tier filter (z-axis 4-6 for compute), a spatial filter (within 50 km), and a capability filter (model loaded, free VRAM, latency budget). Hyperweave returns the top-k candidates ranked by toroidal-Manhattan distance — no central scheduler.
MODEL_AND_DATASET_DISTRIBUTION
Frontier models are 200 GB – 2 TB content-addressed blobs. Hyperweave's CAS layer + chunking + geographic replication delivers a peer-owned distribution path; one replica lives near the uploader so same-region downloads are one hop.
TIER_AWARE_DISPATCH
Hyperweave's z-axis tier packing routes heavy inference jobs to high-tier compute (datacenter GPUs) and small jobs to nearby capable peers. Mixed networks of gaming PCs, workstations, and datacenter clusters compose without manual balancing.
FAULT_TOLERANT_INFERENCE
If a compute node fails mid-stream, the scheduler picks the next candidate from the same discovery result. Hyperweave's SWIM-style liveness signals mark down peers fast; recovery is 3× faster than top-tier DHTs under churn.
Median latency
4.65× faster vs top DHTs
Tail latency (p99)
5× faster vs top DHTs
Failover
3× faster recovery
Churn success
+30% vs top DHTs
Per-node state
O(k + log n)
Replication
r=5, w=3
Zero Central Bottleneck
No API gateway or load balancer to become a chokepoint. Every node can accept and route requests directly through the mesh.
Geographic Locality
Queries automatically route to the nearest capable node. Users in Tokyo get responses from Asia-East, not US-West.
Elastic Scaling
Add or remove compute nodes without reconfiguration. The mesh automatically discovers new capacity and rebalances routes.