Edge Node Network v3.2 Active

Decentralized AI Inference
at the Network Edge

csasq NODES provide hyper-localized, ultra-low latency text classification and embedding generation. By pushing ONNX runtimes and DOM sanitization to the edge, we reduce central GPU bandwidth bottlenecks by up to 99%.

Explore API Documentation View Architecture Topology
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Zero-Latency Ingestion

Our edge workers are deployed in Tier-1 data centers globally. When the central Russian master node dispatches a URL payload via multiplexed WebSockets, the edge worker fetches and parses the HTML locally within milliseconds.

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Local Tensor Computation

We don't send raw text over the wire. Each node runs highly optimized, quantized ONNX models (like all-MiniLM-L6-v2) directly in system memory, generating dense Float32 vectors on the fly.

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Ephemeral Processing

Security and compliance by design. Edge nodes are completely stateless. No logs, no persistent storage, and no cache. As soon as the binary vector is streamed back to the master, memory is immediately zeroed out.

Continuous Streaming via WebSockets

Traditional REST architectures introduce massive HTTP overhead when dealing with high-frequency, small-payload NLP tasks. csasq NODES solve this by establishing a persistent, bi-directional WSS tunnel.

  • Multiplexed Tasking: Master sends thousands of URLs concurrently.
  • Binary Serialization: Vectors are packed into dense binary frames, reducing payload size by 40%.
  • Auto-Reconnect: Built-in jitter tolerance and exponential backoff for unstable long-haul links.
Learn about our WSS Protocol → 
{
  "node_id": "ru-edge-msk-04",
  "status": "connected",
  "wss_throughput": {
    "tx_bytes_sec": 4829104,
    "rx_bytes_sec": 12044
  },
  "active_model": {
    "name": "bge-small-en-v1.5",
    "quantization": "int8",
    "vram_allocation_mb": 412
  },
  "tasks_processed_1h": 142055
}