Maximilian-Winter/paddler

Stateful load balancer custom-tailored for llama.cpp

Paddler

Paddler is an open-source load balancer and reverse proxy designed specifically for optimizing servers running llama.cpp.

Typical strategies like round robin or least connections are not effective for llama.cpp servers, which need slots for continuous batching and concurrent requests.

Paddler overcomes this by maintaining a stateful load balancer that is aware of each server's available slots, ensuring efficient request distribution. Additionally, Paddler uses agents to monitor the health of individual llama.cpp instances, providing feedback to the load balancer for optimal performance. Paddler also supports the dynamic addition or removal of llama.cpp servers, enabling integration with autoscaling tools.

How it Works

Registering llama.cpp Instances

The sequence repeats for each agent. Agents should be installed alongside llama.cpp instance to report their health status to the load balancer.

sequenceDiagram
    participant loadbalancer as Paddler Load Balancer
    participant agent as Paddler Agent
    participant llamacpp as llama.cpp

    agent->>llamacpp: Hey, are you alive?
    llamacpp-->>agent: Yes, this is my health status
    agent-->>loadbalancer: llama.cpp is still working
    loadbalancer->>llamacpp: I have a request for you to handle

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Tutorials

• Installing llama.cpp on AWS EC2 CUDA Instance
• Installing llama.cpp with AWS EC2 Image Builder

Usage

Installation

You can download the latest release from the releases page.

Alternatively you can build the project yourself. You need go>=1.21 and nodejs (for dashboard's front-end code) to build the project.

$ git clone git@github.com:distantmagic/paddler.git
$ cd paddler
$ pushd ./management
$ make esbuild # dashboard front-end
$ popd
$ go build -o paddler

Running Agents

The agent should be installed in the same host as llama.cpp.

It needs a few pieces of information:

1. external-* tells how the load balancer can connect to the llama.cpp instance
2. local-* tells how the agent can connect to the llama.cpp instance
3. management-* tell where the agent should report the health status

./paddler agent \
    --external-llamacpp-host 127.0.0.1 \
    --external-llamacpp-port 8088 \
    --local-llamacpp-host 127.0.0.1 \
    --local-llamacpp-port 8088 \
    --management-host 127.0.0.1 \
    --management-port 8085

Replace hosts and ports with your own server addresses when deploying.

Running Load Balancer

Load balancer collects data from agents and exposes reverse proxy to the outside world.

It requires two sets of flags:

1. management-* tells where the load balancer should listen for updates from agents
2. reverseproxy-* tells how load balancer can be reached from the outside hosts

./paddler balancer \
    --management-host 127.0.0.1 \
    --management-port 8085 \
    --reverseproxy-host 196.168.2.10 \
    --reverseproxy-port 8080

management-host and management-port in agents should be the same as in the load balancer.

You can enable dashboard to see the status of the agents with --management-dashboard-enable=true flag. If enabled it is available at the management server address under /dashboard path.

Changelog

v0.1.0

• New feature: Aggregated Health Status Responses

Roadmap

• llama.cpp reverse proxy
• Basic load balancer
• Circuit breaker
• OpenTelemetry observer
• Integration with AWS Auto Scaling (and other cloud providers) - out of the box endpoint with a custom metric to scale up/down
• Queueing requests

Community

Discord: https://discord.gg/kysUzFqSCK