aidp-neural-cloud-paper

AIDP Neural Cloud: Distributed LLM Inference on Decentralized GPU Networks

Authors: Matthew Karsten (Purple Squirrel Networks)
Date: February 2026
License: MIT

Related Resources

Abstract

We present AIDP Neural Cloud, a distributed large language model (LLM) inference system built on decentralized GPU networks. Our approach leverages geographically distributed GPU nodes to provide OpenAI-compatible LLM inference with significant improvements in both cost efficiency and latency. Through intelligent load balancing and fault-tolerant architecture, we achieve 47% cost reduction and 28% faster latency compared to centralized providers like OpenAI. The system demonstrates scalability to 50 requests per second with automatic failover capabilities, making decentralized GPU compute viable for production LLM deployments.

Key Results

Architecture

+---------------------------------------------------------+
|                  Neural Cloud                           |
+---------------------------------------------------------+
|  API Gateway                                            |
|  +-- /v1/chat/completions (OpenAI-compatible)           |
+---------------------------------------------------------+
|  Load Balancer                                          |
|  +-- Health checks -> Route to fastest node             |
+---------------------------------------------------------+
|  AIDP GPU Workers (N nodes)                             |
|  +-- vLLM inference engine                              |
|  +-- Continuous batching                                |
|  +-- PagedAttention for KV cache                        |
+---------------------------------------------------------+

Quick Start

import openai

client = openai.OpenAI(
    base_url="https://neural-cloud.aidp.store/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="purple-squirrel-r1",
    messages=[
        {"role": "user", "content": "Explain decentralized GPU compute"}
    ]
)
print(response.choices[0].message.content)

Benchmark Results

Latency Comparison

Cost Analysis

Throughput Scalability

Technical Contributions

1. Distributed Architecture: Novel load balancing system routing requests across decentralized GPU nodes
2. Cost Efficiency: 47% reduction in inference costs through decentralized resource pooling
3. Fault Tolerance: Automatic failover with sub-second recovery when nodes go offline
4. OpenAI Compatibility: Drop-in replacement API enabling zero-code migration

Citation

@techreport{karsten2026neuralcloud,
  title={AIDP Neural Cloud: Distributed LLM Inference on Decentralized GPU Networks},
  author={Karsten, Matthew},
  institution={Purple Squirrel Networks},
  year={2026},
  month={February},
  url={https://huggingface.co/purplesquirrelnetworks/aidp-neural-cloud-paper}
}

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