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llmaz

Easy, advanced inference platform for large language models on Kubernetes

stability-alpha GoReport Widget Latest Release

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.

Architecture

architecture

Features Overview

  • Easy of Use: People can quick deploy a LLM service with minimal configurations.
  • Broad Backend Support: llmaz supports a wide range of advanced inference backends for different scenarios, like vLLM, Text-Generation-Inference, SGLang, llama.cpp. Find the full list of supported backends here.
  • Model Distribution: Out-of-the-box model cache system with Manta.
  • Scaling Efficiency (WIP): llmaz works smoothly with autoscaling components like Cluster-Autoscaler or Karpenter to support elastic scenarios.
  • Accelerator Fungibility (WIP): llmaz supports serving the same LLM with various accelerators to optimize cost and performance.
  • SOTA Inference: llmaz supports the latest cutting-edge researches like Speculative Decoding or Splitwise(WIP) to run on Kubernetes.
  • Various Model Providers: llmaz supports a wide range of model providers, such as HuggingFace, ModelScope, ObjectStores(aliyun OSS, more on the way). llmaz automatically handles the model loading requiring no effort from users.
  • Multi-hosts Support: llmaz supports both single-host and multi-hosts scenarios with LWS from day 0.

Quick Start

Installation

Read the Installation for guidance.

Deploy

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

Please refer to examples to learn more.

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
  inferenceFlavors:
  - name: t4 # GPU type
    requests:
      nvidia.com/gpu: 1

Inference Playground

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

Test

Expose the service

kubectl port-forward pod/opt-125m-0 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

If you want to learn more about this project, please refer to develop.md.

Roadmap

  • Gateway support for traffic routing
  • Metrics support
  • Serverless support for cloud-agnostic users
  • CLI tool support
  • Model training, fine tuning in the long-term

Contributions

🚀 All kinds of contributions are welcomed ! Please follow CONTRIBUTING.md.

🎉 Thanks to all these contributors !