What is a Large Language Model?
A large language model is a type of artificial intelligence model that is trained on massive amounts of text data, such as books, articles, and online content. These models are typically neural networks that use deep learning techniques to learn patterns and relationships within the text data. The goal of training these models is to enable them to generate coherent paragraphs of text, respond to user queries, and perform other language-related tasks in a human-like manner. In recent years, the emergence of powerful Large Language Models (LLMs) such as OpenAI’s GPT series, Google Bard, and Meta Llama has made significant strides across various industries. The social impact sector is included.
Large language models work by processing text input and generating text output based on what they have learned from the training data. The training process involves feeding the model vast amounts of text data and adjusting the model’s parameters to optimize its performance. Once the model is trained, it can be used to perform a wide range of language-related tasks, such as language translation, text summarization, and chatbot conversations.
Is hosting an LLM on our own server more cost-effective and secure than using LLM model API services provided by alternative providers like OpenAI?
In recent years, Large Language Models (LLMs) like GPT-3, GPT-4, and others have gained immense popularity for their remarkable language generation capabilities. Businesses and developers have been leveraging these models for various applications, from content generation to chatbots. However, while these models offer great value, they come with associated costs and data security concerns. Let’s dive into the discussion of whether hosting your own LLM on your server could be a more cost-effective and secure alternative to using services like OpenAI.
Section 1: Understanding the Costs of Using AI Model APIs OpenAI and similar LLM providers often charge users per token. The cost per token can vary based on the model you use, with newer models being more expensive. For example, GPT-4 might cost $0.03 per 1000 tokens, while GPT-3.5 might be priced at $0.0015 for the same amount. Consider a scenario where you’re running an AI application that generates blog posts, and each post is under 1500 words. If you use GPT-4 for this task, one blog post would cost around 6 cents. Now, imagine your application receives 1000 requests per day to generate these posts. You’d be looking at an average monthly cost of approximately $1,000.
Section 2: The Alternative – Hosting Your Own LLM on AWS
The idea of hosting your own LLM is fascinating. By doing so, you can have more control over the costs and ensure data security. This is a significant advantage because data is processed and stored on your own server, reducing potential security risks associated with external service providers. The primary cost factor in hosting your LLM is the server type. Different LLM models require different server types. For instance, if you decide to use a powerful model like Llama-2 7B (with 7 billion parameters), you’d need an EC2 g5.2xlarge server instance, which costs approximately $850 per month. Additionally, you’d need to connect the model to an API using AWS API Gateway and AWS Lambda, which would cost less than $100 per month with 1000 requests per day. In this case, your self-hosted LLM would also cost around $1,000 per month, similar to using OpenAI. However, the key difference is that your data remains securely within your infrastructure.
Hosting LLMs on our infrastructure can provide us with greater control, customization options, and data security.
Greater Control: Hosting LLMs on your infrastructure provides complete control over the environment, allowing you to configure it to your specific needs.
Customization: You can customize the server, software, and infrastructure according to your requirements, optimizing the model’s performance.
Data Security: Hosting on your server enhances data security and privacy, especially in highly regulated industries.
Cost Efficiency: While hosting LLMs on your own infrastructure, like AWS EC2, involves cloud-based services with associated charges, it can be more cost-effective for stable workloads.
Hosting LLM on Sagemaker:
What is Amazon SageMaker?
Amazon SageMaker is a managed service in the Amazon Web Services (AWS). It provides the tools to build, train and deploy Large Language Models for predictive analytics applications. The platform automates the tedious work of building a production-ready artificial intelligence (AI) pipeline.
Deploying Large Language Models is challenging. Amazon SageMaker aims to simplify the process. It uses common algorithms and other tools to accelerate the machine learning process.
SageMakerConcepts:
SageMaker Inference Endpoints:
SageMaker supports the ability to perform both batch transform and real-time inference. Batch transform can be a good fit if your inference job is not latency sensitive. Inference endpoints give you the ability to deploy a machine learning model that can serve workloads that require low latency. There are a number of ways to deploy SageMaker inference endpoints. A few common ways to deploy inference endpoints is programmatically through an AWS Software Development Kit (SDK) such as Boto3 or through the SageMaker Console. SageMaker inference endpoints are HTTPS endpoints that you can integrate with a broader application stack. For details on how the HTTPS endpoints are invoked see the AWS API documentation for InvokeEndpoint within SageMaker Runtime. There are many configuration options available for SageMaker inference endpoints, such as auto-scaling, which allows you to scale the resources the endpoint uses to serve your inference requests up or down based on the volume of the requests.
SageMaker JumpStart:
SageMaker JumpStart provides a variety of pre-trained and open-source models, as well as other solutions that you can deploy into your own projects. For this blog post, you will focus on the pre-trained models available in SageMaker JumpStart. By leveraging SageMaker JumpStart, you can deploy LLMs to inference endpoints in one-step via SageMaker Studio,programmatically with an SDK, or in the SageMaker Console. You simply provide arguments to a few parameters.
One parameter you must specify when deploying the model is the size of your instance. Your inference endpoint provides you the ability to send inference requests and receive predictions, your ML model is deployed on an instance, and you can control the size of this instance. Some models have specific instance size requirements. Your model will be hosted on the instance size you specify.
Deploy a SageMaker Endpoint via SageMaker JumpStart:
The process for deploying LLM can be found here. For example, to deploy Llama-2–70B it is recommended to use an ml.g5.48xlarge instance. In addition, you can deploy a different model, but you will likely need to adjust the content handler.
Currently Llama 2 models are available in the following AWS regions at the time of this writing, but please check: us-east 1, us-west 2, eu-west-1, and ap-southeast-1 (refer to the SageMaker JumpStart documentation for more information on current region support).
Once your model is deployed and running you can write the code to interact with your model (we can use langchain).
Advantages of Sagemaker:
1.Amazon SageMaker makes it easier for businesses to build and deploy large language models. It offers the required underlying infrastructure, managed machine learning platform and pre-configured environments.
2.Amazon Sagemaker provides various built-in training algorithms and pre-trained models. You can choose the model according to your requirements for quick model training.
3.Cost-efficient model training.Training deep learning models requires high GPU utilization. Moreover, the CPU-intensive ML algorithms should switch to another instance type with a higher CPU: GPU ratio. With AWS SageMaker heterogeneous clusters, data engineers can easily train the models with multiple instance types. This takes some of the CPU tasks from the GPU instances and transfers them to dedicated compute-optimized CPU instances. This ensures higher GPU utilization as well as faster and more cost-efficient training.
4.Pay-as-you-use model One of the best advantages of Amazon SageMaker is the fee structure. Amazon SageMaker is free to try. As a part of AWS Free Tier, you can get started with Amazon SageMaker for free. Moreover, once the trial period is over, you need to pay only for what you use.
Hosting LLM on EC2 Instance:
EC2 stands for Elastic compute cloud that provides scalable compute capacity in AWS Cloud. You can install software on EC2, and it is easy to deploy your application. It is nothing but you take a server on the cloud on rent.
EC2 is a robust choice for hosting LLMs due to its flexibility and control. With EC2, you have the power to customize the environment to your specific needs. Here’s how you can host an LLM on EC2:
Open EC2 Dashboard in your management console through the search tab or services. As you open the EC2 dashboard, you will observe how many instances are running. Click on the Launch Instance tab to launch a new EC2 Instance. Now by configuring 6-7 steps, you can launch your new Instance.
Advantages of EC2:
1.Deploying LLMs on EC2 may involve more setup and configuration, but it’s an excellent choice for custom requirements.
2.EC2 instances offer saving plans (1-year and 3-year terms), which can significantly reduce costs compared to on-demand pricing, while SageMaker does not offer similar saving plans for all the instances in sagemaker.
3.EC2 is more cost effective than sagemaker Selecting the right hosting option for your LLM depends on your specific needs. If you value control, customization, and cost-efficiency, EC2 may be your best choice. On the other hand, SageMaker’s automation and simplicity can save time and effort, making it ideal for machine learning workflows.
Sagemaker vs EC2:
Ease of Use: Amazon SageMaker is user-friendly, while EC2 requires more manual setup.
Customisation: EC2 offers extensive customisation options, allowing you to tailor the environment to your needs.
Scalability: SageMaker simplifies scaling, making it an ideal choice for dynamic workloads.
Cost: Both services have different pricing models, so you’ll need to compare them based on your usage.
To run LLM we should have a GPU. So we are using GPU based instances(Amazon EC2 G5 Instances have up to 8 NVIDIA A10G GPUs)
EC2 pricing: https://aws.amazon.com/ec2/instance-types/g5/
Sagemaker Pricing:
For on demand: https://aws.amazon.com/sagemaker/pricing/
Available instances for SageMaker Savings Plans: https://aws.amazon.com/savingsplans/ml-pricing/
Note: ml.g5 instances are not eligible for SageMaker Savings Plans.
SageMaker G5 instances are generally around 25% more expensive than their equivalent EC2 G5 instances in terms of on-demand pricing.
EC2 instances offer saving plans (1-year and 3-year terms), which can significantly reduce costs compared to on-demand pricing, while SageMaker does not offer similar saving plans for our required g5 instances.