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OpenLLM: Unlock the Power of Large Language Models

OpenLLM: Unlock the Power of Large Language Models

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Discover how OpenLLM revolutionizes the deployment and operation of large language models in production. Learn about its key features, integrations, and real-world applications.

Are you intrigued by the capabilities of large language models but puzzled about how to deploy and operate them efficiently in a production environment? Look no further! This comprehensive guide will walk you through OpenLLM, a groundbreaking platform that simplifies this complex task. Available on GitHub, OpenLLM is your one-stop solution for running, deploying, and managing large language models.

Whether you're a seasoned data scientist or a curious beginner, understanding OpenLLM can significantly elevate your machine learning projects. This article aims to be your ultimate resource, covering everything from key features and installation steps to real-world applications and integrations with other tools like LangChain and BentoML.

Want to learn the latest LLM News? Check out the latest LLM leaderboard!

What Makes OpenLLM Unique?

So, what is OpenLLM?

What is OpenLLM

OpenLLM stands for Open Large Language Models, and as the name suggests, it's an open platform designed to operate large language models in production settings. One of the most compelling features of OpenLLM is its support for a wide range of state-of-the-art LLMs and model runtimes. Whether you're interested in StableLM, Falcon, Dolly, Flan-T5, ChatGLM, or StarCoder, OpenLLM has got you covered.


Key Features of OpenLLM

  • Fine-Tuning: OpenLLM allows you to fine-tune your models to meet specific requirements. This is particularly useful when you need your model to focus on a particular domain or dataset.

  • Custom Metrics: OpenLLM allows you to define custom metrics for monitoring your models, enabling more nuanced performance tracking.

  • Automated Scaling: With features like Horizontal Pod Autoscaling in Kubernetes, OpenLLM can automatically adjust the number of running instances based on the load, ensuring optimal performance at all times.

  • Serving and Deploying: With OpenLLM, serving and deploying your models is a breeze. You can easily set up a server and make your model accessible to other applications or services.

  • Monitoring: OpenLLM comes with built-in monitoring tools that help you keep an eye on the performance and health of your deployed models.

  • Docker Support: For those who are familiar with containerization, OpenLLM offers Docker support, making it easier to manage and scale your deployments.

  • Cloud Deployment: OpenLLM is compatible with various cloud services, allowing you to leverage the power of cloud computing for your machine learning projects.

  • On-Premises Deployment: If you prefer to keep your data in-house, OpenLLM supports on-premises deployments as well. This is crucial for businesses that handle sensitive or confidential information.

  • Multi-Model Deployments: OpenLLM supports the deployment of multiple models simultaneously, allowing for more complex applications that leverage the strengths of different models.

What about vLLM? What is the difference between OpenLLM and vLLM?

OpenLLM and vLLM are both platforms designed for deploying and managing large language models (LLMs), but they differ in several key aspects:

  • Open Source vs. Proprietary: OpenLLM is an open-source platform, allowing for greater flexibility and customization. vLLM, on the other hand, is often a proprietary solution, which may limit customization options.

  • Community Support: OpenLLM has a strong community of developers and users contributing to its ecosystem, whereas vLLM may not have as extensive a community, depending on the vendor.

  • Feature Set: OpenLLM often comes with a robust set of features for fine-tuning, serving, and monitoring LLMs. vLLM's feature set may vary depending on the vendor and may require additional licenses for advanced features.

  • Deployment Options: OpenLLM offers a variety of deployment options, including local servers, cloud-based solutions, and Kubernetes. vLLM's deployment options may be more limited or tied to specific cloud providers.

  • Cost: Being open-source, OpenLLM can be more cost-effective, especially for smaller projects or individual developers. vLLM may involve licensing fees or other costs.

How OpenLLM Makes LLM Deployment Easier

Deploying large language models in production is not without its challenges. From managing computational resources to ensuring data privacy, there are several issues that you might encounter. OpenLLM provides a range of features designed to help you overcome these challenges.

  • Resource Management: OpenLLM allows for efficient allocation of computational resources, ensuring that your models run smoothly even under heavy load. This is particularly useful for businesses that need to handle a large number of simultaneous requests.

  • Data Privacy: OpenLLM supports on-premises deployments, allowing businesses to keep their data in-house and comply with data privacy regulations.

  • Cost Management: Running large language models can be expensive, especially when deployed at scale. OpenLLM offers features like model pruning and quantization to help manage costs without sacrificing performance.

Deploying large language models in production is not without its challenges. From managing computational resources to ensuring data privacy, there are several issues that you might encounter. OpenLLM provides a range of features designed to help you overcome these challenges.

ChallengeOpenLLM SolutionDescription
Resource ManagementEfficient AllocationManages computational resources for smooth operation under heavy load.
Data PrivacyOn-Premises DeploymentKeeps data in-house for compliance with data privacy regulations.
Cost ManagementModel Pruning and QuantizationManages costs without sacrificing performance.
Custom MetricsCustomizable MetricsAllows nuanced performance tracking.
Automated ScalingHorizontal Pod Autoscaling in KubernetesAutomatically adjusts the number of running instances based on load.
Multi-Model DeploymentSupports Multiple ModelsAllows deployment of multiple models for complex applications.

Getting Started with OpenLLM

How to Use OpenLLM

Step 1. Install OpenLLM

Before you can harness the power of OpenLLM, you'll need to get it up and running on your system. The installation process is straightforward and can be completed in just a few steps. OpenLLM is available on PyPI, which means you can install it using Python's package manager, pip.

pip install openllm

This single command will download and install OpenLLM, along with any required dependencies. Make sure you have Python 3.8 or higher installed on your system for a smooth installation process.

  • Python Version: OpenLLM requires Python 3.8 or higher. You can check your Python version by running python --version in your terminal.

  • Package Dependencies: The pip install openllm command will also install any required package dependencies automatically, so you don't have to worry about missing out on any crucial components.

  • Virtual Environment: It's a good practice to install Python packages in a virtual environment to avoid any conflicts with system-wide packages. You can create a virtual environment using python -m venv myenv and activate it before running the pip command.

By following these detailed steps, you'll have OpenLLM installed and ready for action in no time.

Step 2. Running Your First OpenLLM App

Once you've installed OpenLLM, you're all set to run your first OpenLLM app. Starting an LLM server locally is as simple as executing a single command. For instance, if you want to start a Dolly v2 model, you can do so with the following command:

openllm start dolly-v2

This command will initialize the Dolly v2 model and start the OpenLLM server, making it accessible for other applications or services to interact with.

  • Port Configuration: By default, the OpenLLM server runs on port 5000. However, you can specify a different port using the --port flag, like so: openllm start dolly-v2 --port 6000.

  • Logging: OpenLLM provides detailed logs that can help you debug or optimize your models. You can specify the level of logging detail with the --log-level flag.

  • Resource Allocation: If you're running multiple models, OpenLLM allows you to allocate specific resources to each one, ensuring optimal performance.

Running your first OpenLLM app is that simple! You now have a working large language model running locally, ready to be integrated into your projects or deployed in a production environment.

How to Deploy OpenLLM with Docker and Kubernetes

Step 1. Setting Up Your Environment for Open LLM

Before you can deploy your large language models with OpenLLM, it's essential to prepare your environment. This involves several steps, including installing NVIDIA GPU drivers, CUDA libraries, and setting up Kubernetes with GPU support. Each of these components plays a crucial role in enabling GPU acceleration, which is vital for maximizing the performance of your large language models.

Step 1.1. Installing NVIDIA GPU Drivers

Firstly, you'll need to install the NVIDIA GPU drivers to enable GPU support on your machine. You can do this with the following command:

sudo apt-get update && sudo apt-get install -y nvidia-driver-460

After installation, reboot your machine to activate the drivers.

Step 1.2. Installing CUDA Libraries

Next, you'll need to install the CUDA toolkit, which provides the development environment for GPU-accelerated applications. Use the following command to install CUDA 11.0:

sudo apt-get update && sudo apt-get install -y cuda-11-0

After installing, add CUDA to your PATH:

echo 'export PATH=/usr/local/cuda-11.0/bin:$PATH' >> ~/.bashrc
source ~/.bashrc

Step 1.3. Installing Kubernetes and Minikube

To set up a local Kubernetes cluster, you can use Minikube. Install it with the following command:

sudo apt-get update && sudo apt-get install -y minikube

Once Minikube is installed, start it with GPU support:

minikube start --driver=nvidia

This will start a local Kubernetes cluster with NVIDIA GPU support.

Step 1.4. Enabling Kubernetes GPU Support

Finally, to enable GPU support in your Kubernetes cluster, you'll need to deploy the NVIDIA device plugin. Use the following command to do so:

kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.9.0/nvidia-device-plugin.yml

This will deploy the NVIDIA device plugin to your cluster, enabling GPU support for your pods.

By following these detailed steps, you'll set up an environment that's ready for deploying large language models with OpenLLM, fully optimized for GPU acceleration.

Step 2. Containerizing and Loading Models with OpenLLM

OpenLLM allows you to containerize your large language models and load them into a Docker container. This is particularly useful for ensuring a consistent runtime environment across different deployments. To containerize your model, you can use the following command:

openllm build dolly-v2 --model-id databricks/dolly-v2-3b

This will package your LLM model, OpenLLM dependencies, and other relevant libraries within a Docker container. To generate an OCI-compatible Docker image, run:

bentoml containerize <name:version> -t dolly-v2-3b:latest --opt progress=plain

Step 3. Deploying on Kubernetes

Kubernetes offers features like Horizontal Pod Autoscaling (HPA) that can help efficiently scale your model for production use. You can set up communication protocols within pods to manage model input and output, either through RESTful APIs or gRPC-based communication. OpenLLM has a gRPC server running by default on port 3000. A sample Kubernetes deployment file could look like this:

apiVersion: apps/v1
kind: Deployment
 name: dolly-v2-deployment
 replicas: 3
     app: dolly-v2
       app: dolly-v2
     - name: dolly-v2
       image: dolly-v2-3b:latest
       imagePullPolicy: Never
       - containerPort: 3000

For autoscaling, you can configure HPAs to automatically adjust the number of pods based on CPU or custom metrics. This ensures optimal resource utilization.

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
 name: dolly-v2-hpa
   apiVersion: apps/v1
   kind: Deployment
   name: dolly-v2-deployment
 minReplicas: 1
 maxReplicas: 10
 targetCPUUtilizationPercentage: 60

By leveraging Docker and Kubernetes, OpenLLM allows for a seamless and scalable deployment process, ensuring that your large language models are both performant and cost-effective.


OpenLLM is a groundbreaking platform that revolutionizes the way we deploy and operate large language models. With its robust set of features, including fine-tuning, serving, deployment, and monitoring, OpenLLM simplifies what would otherwise be a complex and resource-intensive process. Its versatility in deployment options, from local servers to cloud-based solutions and even Kubernetes, makes it a one-size-fits-all solution for both individual developers and large organizations.

Whether you're looking to automate customer service, generate content, or provide personalized healthcare solutions, OpenLLM has the tools and features to make your project a success. With its strong community support and extensive documentation, getting started with OpenLLM is easier than ever.

So why wait? Dive into the world of large language models and discover how OpenLLM can take your projects to the next level.

Want to learn the latest LLM News? Check out the latest LLM leaderboard!

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