EDGE.GRID
System: Distributed Intelligence

SWARM
COMPUTE.

Harness the collective power of 142,000+ decentralized GPUs. Train LLMs and run edge inference with zero central point of failure.

Total Network Capacity
84,205 TFLOPs
Active Edge Nodes
142,891
Consensus State
Optimized

Resource Allocation

Pricing dynamic based on network load
Layer 1 // Edge Inference

API Routing

Low-latency model inference routed to the nearest geographic edge node.

  • Latency < 15ms
  • Hardware RTX 4090 / A4000
  • Auto-scaling Infinite
$0.0002
Per 1K Tokens
Layer 2 // Swarm Training POPULAR

Cluster Setup

Distributed training across heterogeneous GPU swarms with fault tolerance.

  • Framework PyTorch / Ray
  • Hardware A100 / H100 Mix
  • Fault Tolerance Automated
$0.45
Per TFLOP-Hr
Layer 3 // Bare Metal

Dedicated Node

Exclusive access to Tier 4 datacenter hardware. Zero virtualization overhead.

  • Access Root SSH
  • Hardware 8x H100 SXM
  • Interconnect NVSwitch
Custom
Enterprise SLA

Seamless Integration

Replace your existing OpenAI or Anthropic endpoints with a single line of code. The swarm handles load balancing and node discovery automatically.

Python
import edgegrid

# Initialize connection to the decentralized swarm
client = edgegrid.SwarmClient(api_key="YOUR_EDGE_KEY")

# Submit training job to distributed nodes
job = client.train.submit(
    model="llama-3-8b-base",
    dataset="s3://bucket/data.jsonl",
    strategy="pipeline_parallel",
    max_nodes=128
)

print(f"Job active. Allocated {job.tflops_assigned} TFLOPs.")
Action successful.