GLOBAL_LLM_INFRASTRUCTURE
Distributed AI Inference at Planetary Scale
Run large language models across the globe instead of a single data center. 35% faster response times and 8× lower network overhead for enterprise AI through geo-intelligent routing.
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
EDGE_INFERENCE_ROUTING
User queries automatically route to the nearest capable node. No central load balancer—the mesh itself determines optimal paths based on real-time capacity and latency.
MODEL_SHARD_DISTRIBUTION
Large models are sharded across geographic regions. Each region maintains hot copies of frequently-used layers while cold layers are fetched on-demand through the mesh.
COMPUTE_TIER_AWARENESS
Hyperweave's performance layer automatically identifies high-capability nodes (GPU clusters, TPU farms) and routes complex inference tasks accordingly.
FAULT_TOLERANT_INFERENCE
If a compute node fails mid-inference, requests automatically failover to the next nearest capable node. Users experience momentary latency increase, never failures.
Query Routing
< 5ms
First Token
< 200ms
Failover Time
< 50ms
Model Sync
Eventual
Max Model Size
Unlimited
Global Nodes
1000+
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.