GitHub - paguasmar/veloxml-deploy: Deploy LLMs to production in one command. VeloxML CLI is an open-source engine that provisions optimized serverless infrastructure directly in your own AWS/GCP account

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Deploy open-source LLMs directly to your own AWS/GCP account with one command. Zero Docker, zero Kubernetes, scale-to-zero.

VeloxML Demo

Contents

Status

  • Alpha: Under heavy development
  • Beta: Ready for use. But go easy on us, there may be a few kinks.
  • 1.0: Use in production!

This repo is still under heavy development and the documentation is evolving. You're welcome to try it, but expect some breaking changes. Watch "releases" of this repo to receive a notification when we are ready for Beta. And give us a star if you like it!

Example

# 1. Initialize a model service
veloxml init my-model
cd my-model

# 2. Deploy to AWS with scale-to-zero
veloxml deploy

# Output:
# Replica ready at http://34.201.45.12:8000
# Test your endpoint:
# curl -X POST http://34.201.45.12:8000/predict \
#   -H "Content-Type: application/json" \
#   -d '{"prompt": "Hello world"}'

Introduction

What is this?

This is a CLI and deployment engine that allows you to deploy open-source LLMs directly to your own cloud account (AWS/GCP) with a single command.

It works like this:

  1. the CLI reads your model code (app.py) and hardware spec (veloxml.yaml)
  2. it provisions an optimized Spot or On-Demand instance (via SkyPilot) directly inside your cloud account
  3. it prepares the runtime, loads the weights, verifies the health probe, and prints a ready-to-test curl command.

Cool, but why not just use Modal, RunPod, or Baseten?

A few reasons:

  1. Your data, prompts, and model weights never leave your own AWS/GCP account. Zero third-party servers, and zero SOC2 or HIPAA compliance headaches
  2. You don't have to pay a $50k-$100k enterprise paywall just to deploy inside your private VPC. VeloxML gives you that exact serverless experience natively in your account on day one
  3. Zero framework lock-in. Modal forces you to rewrite your code with proprietary decorators (@modal.function)

What are the benefits?

  1. The beauty of deploying directly to your own cloud account is that your proprietary data, customer queries, and model weights never leave your security perimeter. Zero third-party compliance reviews (SOC2/HIPAA) needed.
  2. Cost efficiency. VeloxML defaults to Spot instances (use_spot: true), allowing you to serve models on AWS without burning $1,500+/mo on idle, unmanaged GPUs.
  3. This is built on SkyPilot, an extremely robust open-source compute orchestrator developed at UC Berkeley.

What can I build with this?

  1. Private LLM inference APIs (any open-weights checkpoint or fine-tuned model)
  2. Custom embedding & reranking microservices
  3. Real-time reasoning and agent tool-calling backends
  4. Domain-specific fine-tuned models hosted securely inside your VPC
  5. High-throughput batch inference endpoints

Getting started

Deploy a real, open-weights Small Language Model (Qwen/Qwen2.5-0.5B-Instruct) directly to your AWS account on a Spot instance (~$0.07/hr) in under 2 minutes.

  1. Install and verify cloud access
pip install veloxml-deploy
veloxml check
  1. Create project folder
mkdir llm-service
cd llm-service
  1. Create app.py

Paste this into app.py:

from fastapi import FastAPI
from transformers import pipeline

app = FastAPI()
pipe = pipeline("text-generation", model="Qwen/Qwen2.5-0.5B-Instruct")

@app.get("/health")
def health():
    return {"status": "ok"}

@app.post("/predict")
def predict(data: dict):
    return {"response": pipe([{"role": "user", "content": data["prompt"]}], max_new_tokens=50)[0]["generated_text"][-1]["content"]}
  1. Create veloxml.yaml

Paste this into veloxml.yaml:

name: llm-service
compute:
  cpus: 4+
  memory: 8+
  use_spot: true
runtime:
  setup: pip install fastapi uvicorn "transformers<5.0.0" accelerate
  1. Deploy to the cloud

Run:

VeloxML provisions the AWS Spot instance, installs dependencies, verifies the /health probe, and outputs your live replica URL.

  1. Test your live endpoint

Query your live inference API using curl:

curl -X POST http://<ENDPOINT_IP>:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Say this is a test"}'

Output:

{
  "response": "This is a test."
}
  1. Clean Up

When finished testing, terminate all cloud compute to avoid lingering charges:

Contributing

We welcome any issues, pull requests, and feedback. See CONTRIBUTING.md for local development setup.

License

This repo is licensed under Apache 2.0.

Credits