github.com/InftyAI/llmaz @v0.1.4

<img alt="llmaz" src="https://raw.githubusercontent.com/inftyai/llmaz/main/site/static/images/logo.png" width=55%>

Easy, advanced inference platform for large language models on Kubernetes

llmaz (pronounced /lima:z/), aims to provide a Production-Ready inference platform for large language models on Kubernetes. It closely integrates with the state-of-the-art inference backends to bring the leading-edge researches to cloud.

🌱 llmaz is alpha now, so API may change before graduating to Beta.

Overview

<img alt="infrastructure" src="https://raw.githubusercontent.com/inftyai/llmaz/main/site/static/images/infra.png" width=70%>

Architecture

<img alt="architecture" src="https://raw.githubusercontent.com/inftyai/llmaz/main/site/static/images/arch.png" width=100%>

Key Features

• Easy of Use: People can quick deploy a LLM service with minimal configurations.
• Broad Backends Support: llmaz supports a wide range of advanced inference backends for different scenarios, like vLLM, Text-Generation-Inference, SGLang, llama.cpp, TensorRT-LLM. Find the full list of supported backends here.
• Heterogeneous Cluster Support: llmaz supports serving the same LLM with heterogeneous devices together with InftyAI Scheduler for the sake of cost and performance.
• Various Model Providers: llmaz supports a wide range of model providers, such as HuggingFace, ModelScope, ObjectStores. llmaz will automatically handle the model loading, requiring no effort from users.
• Distributed Inference: Multi-host & homogeneous xPyD support with LWS from day 0. Will implement the heterogeneous xPyD in the future.
• AI Gateway Support: Offering capabilities like token-based rate limiting, model routing with the integration of Envoy AI Gateway.
• Scaling Efficiency: Horizontal Pod scaling with HPA with LLM-based metrics and node(spot instance) autoscaling with Karpenter.
• Build-in ChatUI: Out-of-the-box chatbot support with the integration of Open WebUI, offering capacities like function call, RAG, web search and more, see configurations here.

Quick Start

Installation

Read the Installation for guidance.

Deploy

Here's a toy example for deploying facebook/opt-125m, all you need to do is to apply a Model and a Playground.

If you're running on CPUs, you can refer to llama.cpp.

Note: if your model needs Huggingface token for weight downloads, please run kubectl create secret generic modelhub-secret --from-literal=HF_TOKEN=<your token> ahead.

Model

apiVersion: llmaz.io/v1alpha1
kind: OpenModel
metadata:
  name: opt-125m
spec:
  familyName: opt
  source:
    modelHub:
      modelID: facebook/opt-125m
  inferenceConfig:
    flavors:
      - name: default # Configure GPU type
        limits:
          nvidia.com/gpu: 1

Inference Playground

apiVersion: inference.llmaz.io/v1alpha1
kind: Playground
metadata:
  name: opt-125m
spec:
  replicas: 1
  modelClaim:
    modelName: opt-125m

Verify

Expose the service

By default, llmaz will create a ClusterIP service named like <service>-lb for load balancing.

kubectl port-forward svc/opt-125m-lb 8080:8080

Get registered models

curl http://localhost:8080/v1/models

Request a query

curl http://localhost:8080/v1/completions \
-H "Content-Type: application/json" \
-d '{
    "model": "opt-125m",
    "prompt": "San Francisco is a",
    "max_tokens": 10,
    "temperature": 0
}'

More than quick-start

Please refer to examples for more tutorials or read develop.md to learn more about the project.

Roadmap

• Serverless support for cloud-agnostic users
• Prefill-Decode disaggregated serving
• KV cache offload support
• Model training, fine tuning in the long-term

Community

Join us for more discussions:

• Slack Channel: #llmaz

Contributions

All kinds of contributions are welcomed ! Please following CONTRIBUTING.md.

We also have an official fundraising venue through OpenCollective. We'll use the fund transparently to support the development, maintenance, and adoption of our project.

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