Auctor Core Engine
The Auctor Core Engine operates as a distributed ensemble of quantized LLMs optimized for real-time blockchain decision-making. Built on PyTorch's CUDA-accelerated transformer kernels with 8-bit quantization (via LLM.int8()), it achieves 3.2x faster inference compared to FP32 models. The WebRTC data channels (aiortc) provide sub-50ms latency for cross-node communication, while gRPC handles bulk data transfers.
Merkle-rooted model versioning ensures all nodes validate model consistency using SHA3-256 hashes of model parameters. During inference, the engine employs speculative decoding - predicting 3 token candidates in parallel and verifying via a lightweight verification head, achieving 22 tokens/sec/node at 40% reduced compute cost.
Auctor AI leverages a hybrid architecture designed for speed, security, and scalability
Core Components
Auctor Engine: The backbone of the platform, hosting LLMs optimized for decentralized decision-making.
Neural Adaptation Layer (NAL): Enables fine-tuning across heterogeneous blockchain data.
Alpha Grid: A decentralized network of Deep Agents and Perpetuals for distributed intelligence.
Quantum Shield Layer: Ensures security against future quantum threats by integrating post-quantum cryptographic methods.
Data Flow
Input: Multi-modal data ingestion from blockchains, APIs, and IoT.
Processing: Real-time fusion via LLMs and neural networks.
Output: Optimized actions deployed to on-chain environments.
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