Anyone interested in being able to create and run private AI large language models might be interested in this quick guide which provides more information on running Llama 2 with a smaller context and achieving the full 32,000 tokens requiring a GPU rental.
This can cost anywhere between 70 cents to $1.50 per hour, depending on the platform and the specific requirements of the user. This article aims to provide a comprehensive guide on how to achieve this feat, using an 80GB A100 GPU on platforms such as RunPod, AWS, or Azure.
One of the most accessible platforms for beginners is Google Collab. Here, a free notebook can handle up to 8,000 tokens but you can also double this capacity to 16,000 tokens. Check out the video below kindly created by Trelis Research to learn more. This is a great starting point for those new to the field, providing a solid foundation before moving on to more complex tasks.
For those seeking better quality, you can run Llama with a 13B model. However, it’s important to note that this comes with a trade-off. The larger model size reduces the context length to 16k, but the quality of the output is significantly improved.
Other articles you may find of interest on the subject of Llama 2 :
PRO Notebooks
Code Llama PRO 32k Context Length offers a range of features, including the ability to save chats, reload chats, adjust context lengths, and upload files for discussion. These features provide a more interactive and user-friendly experience, making the process of running Llama 2 more efficient and enjoyable. It is available to purchase for €9.99.
- Allows for saving and re-loading of conversations
- Allows for uploading and analysis of documents
- Works on Google Colab or on a Server (e.g. AWS, Azure, RunPod)
RunPod Key features
- GPU Instances: These are container-based GPU instances that are deployable in seconds. They come in two flavors: Secure Cloud and Community Cloud. Secure Cloud operates in T3/T4 data centers for high security and reliability, while Community Cloud offers a peer-to-peer system connecting individual compute providers to consumers.
- Serverless GPUs: This is a pay-per-second serverless GPU computing service, aiming to bring autoscaling to production environments. It is part of the Secure Cloud offering and guarantees low cold-start times along with strong security measures.
- AI Endpoints: These are fully managed and scalable endpoints designed for various AI and ML applications such as Dreambooth, Stable Diffusion, and Whisper.
Additional features:
- CLI / GraphQL API: For automating workflows and managing compute jobs.
- Multiple Access Points: Users can code, optimize, and run jobs through various interfaces like SSH, TCP Ports, and HTTP Ports.
- OnDemand and Spot GPUs: To cater to different computational needs.
- Persistent Volumes: Ensures data safety even when pods are stopped.
- Cloud Sync: Allows for seamless data transfer to any cloud storage.
RunPod offers a comprehensive solution for AI and ML workloads, offering both traditional cloud-based and serverless compute options along with specialized features for AI and machine learning. It competes in the same space as other cloud providers like AWS and Azure but seems to offer features tailored specifically for AI/ML projects.
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