language-pipes 1.0.0

Peer-to-peer distributed inference for open-source language models

Language Pipes

Peer-to-peer distributed inference for open-source language models

Language Pipes is an open-source distributed inference system built on the transformers library that splits large language model computation across multiple machines. By separating the model's text-handling components (embedding and output head) from its intermediate transformer layers, Language Pipes enables peer-to-peer inference.

Features

• OpenAI-compatible API
• Interactive setup wizard
• Automatic model download by HuggingFace ID
• Privacy-oriented architecture with layered privacy mitigations
• Decentralized peer-to-peer network with optional AES encryption

---

How It Works

Language models process input through a sequence of transformer layers. Each layer performs matrix multiplications between learned weights and a hidden state tensor, passing the result to the next layer. Language Pipes distributes these layers across machines, splitting the memory cost across the network while keeping the text-handling components on the origin node.

The architecture provides architectural separation: layer models operate on continuous-valued tensors rather than discrete text while the end models keep text data on trusted systems. The privacy documentation provides a probabilistic threat model that quantifies the difficulty of known inversion attacks under various mitigation configurations.

Further reading:

• Architecture Overview: runtime components and inference flow
• Job Processor State Machine: how jobs traverse the distributed pipeline

Installation

Requires Python 3.10+. For GPU support, install the appropriate PyTorch version for your CUDA configuration:
https://pytorch.org/get-started/locally/

Install from pip:

pip install language-pipes

Quick Start

The easiest way to get started is with the interactive setup wizard:

language-pipes

This launches a menu where you can create, view, and load configurations. Select Create Config to walk through the setup wizard, which guides you through your first configuration. After creating a config, select Load Config to start the server.

Configuration can also be specified via TOML files. See the CLI reference for details on loading configurations from the command line.

---

Two Node Example

This example shows how to distribute a model across two computers using the interactive wizard.

Node 1 (First Computer)

Start language pipes:

language-pipes

Prompt	Value	Description
Node ID	node-1	Unique identifier for this node on the network
Add layer models	Y	Starts layer model editor
Model ID	Qwen/Qwen3-1.7B	HuggingFace model to load for layers
Device	cpu	Hardware to run inference on
Max memory	1	GB of RAM to use (loads part of the model)
Add end model	Y	starts end node editor
Model ID	Qwen/Qwen3-1.7B	Huggingface model to load for ends
Number of local layers	1	Ensure first layer is loaded on your machine
Enable OpenAI API	Y	Exposes the OpenAI-compatible endpoint
API port	8000	Port for the API server
First node in network	Y	This node starts the network
Peer port	5000	Port for peer-to-peer communication
Network IP	[Your local IP]	IP address other nodes can find you at
Encrypt network traffic	N	Disable encryption for simplicity
Advanced options	N	Logging, security, etc.

Node 2 (Second Computer)

Start language pipes with this command:

language-pipes

Prompt	Value	Description
Node ID	node-2	Unique identifier for this node on the network
Add layer models	Y	Starts layer model editor
Model ID	Qwen/Qwen3-1.7B	HuggingFace model to load for layers
Device	cpu	Hardware to run inference on
Max memory	2	GB of RAM to use (loads part of the model)
Add end model	N	starts end node editor
Enable OpenAI API	Y	Exposes the OpenAI-compatible endpoint
API port	8000	Port for the API server
First node in network	N	This node starts the network
Bootstrap Address	[node-1 IP]	IP address of node-1
Peer port	5000	Port for peer-to-peer communication
Encrypt network traffic	N	Disable encryption for simplicity
Advanced options	N	Logging, security, etc.

Node-2 connects to node-1 and loads the remaining model layers. The model is now distributed across both machines and ready for inference.

Test the API

The model is accessible via the OpenAI-compatible API.

Example using the OpenAI Python library:

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:8000/v1",  # node-1 IP address
    api_key="not-needed"  # API key not required for Language Pipes
)

response = client.chat.completions.create(
    model="Qwen/Qwen3-1.7B",
    max_completion_tokens=100,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a haiku about distributed systems."}
    ]
)

print(response.choices[0].message.content)

Install the OpenAI library with: pip install openai

See the OpenAI-compatible API documentation for the full endpoint reference and sampling parameter descriptions.

Supported Models

Language Pipes currently targets the Qwen3 and Qwen3-MoE architectures.

Planned Improvements

• Additional model architectures
• INT8 and INT4 quantization (currently all inference uses fp16)
• GGUF format support (currently requires safetensors)
• /v1/responses endpoint (currently only /v1/chat/completions)

Dependencies

• pytorch
• transformers

Documentation

• CLI Reference
• Privacy Architecture
• SipIt Case Study
• Configuration Manual
• Architecture Overview
• OpenAI-Compatible API
• Job Processor State Machine
• Distributed State Network
• LLM Layer Collector