I Stopped Restarting HTTP Connections Between AI Models. Here Is What I Use Instead.

The article discusses a novel approach to serving large language models (LLMs) and other computationally intensive machine learning tasks by distributing them across multiple machines, each optimized for specific types of workloads. This method leverages Pilot Protocol, an open-source project that enables efficient inter-process communication over the network with persistent connections between components.

Key Points

1. Distributed Model Chaining: The approach involves breaking down a complex pipeline into smaller tasks and distributing them across multiple machines. Each machine runs specialized software (agents) for specific types of models or tasks, such as LLMs, speech-to-text converters (like Whisper), and image generators.

2. Persistent Tunnels: Pilot Protocol establishes persistent network connections between these agents, which allows data to flow efficiently without the overhead of repeated TCP handshakes or HTTP requests. This is particularly beneficial for sustained traffic where the initial connection setup cost can be amortized over many requests.

3. Orchestrator and Agents: The orchestrator manages the overall pipeline, discovering available agents based on their capabilities (tags) and routing requests through persistent tunnels to the appropriate agent. This allows for dynamic scaling and efficient resource utilization.

4. Advantages:

   • **Scalability

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