A Comprehensive Guide to LlaMA 2 hosting

Sam Naji, Joseph Tekriti
October 16, 2023
5 minute read
Table of Contents


LlaMA 2 is a strong natural language processing and generating tool that is popular among researchers and developers. As a result, there is an increasing interest in hosting LlaMA 2 models on other platforms to allow for seamless integration with other applications and services. In this post, we will look at the significance of hosting LlaMA 2, the important elements that influence its cost and requirements, and several choices for safely and efficiently hosting it. LlaMA 2 is a powerful language model that can be used for a variety of tasks, including:

  • Generating text
  • Translating languages
  • Writing different kinds of creative content
  • Answering your questions in an informative way
  • Chatbot
  • Code Generation

Why is hosting LlaMA 2 important?

Hosting LlaMA 2 is crucial because it lets anyone in the world view and use the model. The model has undergone pretraining using a vast corpus of 2 trillion tokens from publicly available data. Its purpose is to facilitate the development of generative AI-driven tools and experiences by developers and organizations. This can be useful in a number of ways, including:

  • Making and testing new apps for language processing
  • Putting language processing models into real-world settings
  • Researchers and producers should be able to use language processing models.




Get permission to access LlaMA 2

Before you planning to host LlaMA 2 keep in mind. As of July 19,2023, Meta has LlaMA 2 gated behind a registration flow. You must initially request access from Meta. Then, you can access from HuggingFace so that we can download the model.

Impact of different context window sizes on the host and requirements for hosting

Size is one of the most important things to think about when picking a language model like LlaMA 2. How much computing power is needed to run the model depends on how big it is, which in turn affects the hosting needs. In this part, we'll talk about how different LlaMA 2 model sizes affect the host and what's needed to host.

Running Requirements

LlaMA2 hosting needs will vary with each application and the complexity of the model being used. The technical requirements for running large language models (LLMs)differ depending on the model's size and complexity. It is critical to have enough VRAM and GPU capacity to satisfy the model's computational demands for good results.

7B Model

  • At least 10GB VRAM (8GB may be sufficient in some cases)
  • GPU like the RTX 3090 or RTX 4090

13B Model

  • At least 12GB VRAM
  • GPU like the RTX 3090 or RTX 4090

70B Model

  • At least 80GB VRAM
  • GPU like the A100 80GB

It is important to note that these are just minimum requirements. For best performance, it is recommended to use a GPU with more VRAM and a faster processor. It is also important to have a stable internet connection when running LLMs.


Cost Analysis of Hosting LlaMA Models on Cloud Platforms

When training or deploying the LlaMA model, one of the significant considerations is the cost of GPU instances across different cloud providers. The choice of cloud provider and instance type can have a considerable impact on your project's budget. For instance to considering a cloud provider to host your LlaMA 70Bmodel, bear in mind that servers with A100 80Gb GPUs are not always accessible. This is due to the growing demand for these GPUs in a number of applications such as machine learning, artificial intelligence, and data science. However, you must verify with your cloud provider that the GPU you select is compatible with the LlaMA model you intend to host.

If you intend to host a LlaMA model on a cloud platform, you should contact the cloud provider for the most up-to-date pricing and availability information. Moreover, in general, you can expect to pay between $0.53 and $7.34 per hour. The cost of hosting the LlaMA 70B models on the three largest cloud providers is estimated in the figure below.

For those leaning towards the 7B model, AWS and Azure start at a competitive rate of $0.53/hr, though Azure can climb up to $0.90/hr. Meanwhile, GCP stands slightly higher at $0.84/hr. Stepping up to the 13B model, AWS remains an appealing choice at $1.01/hr. However, GCP and Azure push the bar to $1.65/hr and a range of $1.80 to $2.07/hr, respectively. This analysis is a vital resource for organizations seeking to exploit the potential of LlaMA models, as it provides a thorough understanding of the financial implications involved. When configuring a machine to execute these models, it is essential to consider the quota limits imposed by each GCP account. These restrictions serve as a safeguard to prevent the unrestricted use of high-end devices and equipment, thereby assisting with cost management.

Case studies: Examples of LlaMA 2 deployments in the cloud

Let's look at some real-world instances of successful deployments of LlaMA 2 on the cloud to get a feel for how it's done. Here are three examples of how LlaMA 2 has been put to use successfully in non-traditional settings.

Case Study 1: Chatbot for Customer Service

The goal of implementing a chatbot was to improve customer service at a major telecom by redirecting human agents' time and energy from routine questions to more complex ones. They decided to build their chatbot on top of LlaMA 2 because of its superior understanding of natural language and its capacity to produce responses that sound more human. LlaMA 2 was deployed on Amazon Web Services (AWS) utilizing a combination of EC2 and S3 instances. Customers can now use either text or voice messages to communicate with the chatbot after LlaMA 2 was integrated into their existing customer support platform. The chatbot performed admirably, answering over 80% of questions from customers without any help from a human being. This led to significant savings and increased customer happiness.

Llama Demo

Case Study 2: Sentiment Analysis for Political Campaigns

A political campaign intended to gauge public opinion by analyzing social media reactions to their candidate and their opponents. They went with LlaMA 2 because of its sophisticated sentiment analysis reports and its ability to process natural language.

The campaign used a mix of Virtual Machines and Storage instances on Microsoft Azure to roll out LlaMA 2. They used LlaMA 2 in conjunction with their social media monitoring tools to examine mentions of their candidate and their opponents. After using LlaMA 2, they saw a spike in support for their candidate that informed their strategy and message.

Model size to choose.

Understanding your expected usage is a key factor some recommendations to choose the model are throughput time, how much utilization and how long will the prompts take to complete on average. Our proposals are as follow:

  • 70b: For chat-bots, logic, questions answers and coding
  • 13b: For writing blogs, stories and poems
  • 7b: To summarize content and categorization

Where to start?

In this blog we provide the resource to our readers if they are not sure where and how to start hosting LlaMA 2 on cloud. For this purpose, a tutorial to hosting LlaMA 2 on Azure as Microsoft and Meta expand their AI partnership with LlaMA 2 on Azure and Windows. LlaMA 2 is now available in the model catalog in Azure Machine Learning the model catalogue, which is presently in public preview in Azure Machine Learning, serves as a central repository for foundation models and enables users to discover, customize, and deploy foundation models at scale.


In conclusion, LlaMA 2 is a powerful tool for natural language processing that has a wide range of applications in industries such as customer service, media and entertainment, and politics. Its ability to understand natural language and generate human-like responses makes it an ideal choice for chatbots, content generation, and sentiment analysis.

Deploying LlaMA 2 in the cloud offers several advantages, including scalability, flexibility, and cost savings. By choosing the right cloud provider and configuring the infrastructure appropriately, businesses can ensure high performance and reliability for their LlaMA 2 deployments.

Whether you’re looking to improve customer engagement, automate content generation, or gain insights into public opinion, LlaMA 2 is a versatile tool that can help you achieve your goals. With the right deployment strategy and use case, businesses can unlock the full potential of LlaMA 2 and drive innovation in their industry.

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