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Everything you need to know about integrating, deploying, and scaling with Aigenix's autonomous AI nodes.
Our platform utilizes localized swarm coordinators that execute edge-level task scheduling. Instead of pinging a central cloud server, local nodes negotiate task routing protocols autonomously using a low-overhead consensus layer, keeping decision latency under 0.08ms.
The swarm routing engine instantly detects the node heartbeat failure (<0.1s) and redistributes the active task payload to adjacent nodes. Weight backups and local states are restored dynamically from the distributed cluster ledger.
Billing is based on actual floating-point operations (FLOPs) processed by the edge nodes. Aigenix normalizes CPU, GPU, and TPU execution times into standard Compute Units (CUs), billing you only for active execution time down to the millisecond.
We support all standard transformer architectures, including LLaMA, Mistral, Phi-3, Gemma, and BERT variants. Custom convolutional nets or vector search algorithms can also be loaded into the node clusters.
Yes. The neural engine can be containerized and deployed to local area networks (LANs) or air-gapped hardware. Once local node clusters are synchronized, they execute swarm heuristics and inference without any external internet connection.
Every node sync packet is cryptographically signed and encrypted using military-grade post-quantum cryptography. Zero-trust handshakes are required for any weight updates, ensuring zero unauthorized model injection.
Yes. Aigenix features native connection adapters for AWS Lambda/SageMaker, Azure ML, and HuggingFace spaces. You can synchronize weights from these libraries and export node state files directly as standard GGUF or ONNX formats.
There are no hard throttles. Since Aigenix scales horizontally across your own cluster nodes, the query capacity expands dynamically as you mount new hardware nodes to the cluster.