How to Choose the Right GPU for Large Language Models

About a week ago, I showed you how to run large language models using cloud GPUs with Hyperstack. If you haven’t seen it, I highly recommend you check it out. The main purpose of that video was to demonstrate that even if you don’t have compatible hardware to run any sort of large language model, you can still utilize Hyperstack’s cloud GPU service to access the necessary computational power. In this article, we will dive deeper into the details and showcase which GPUs are needed for various large language models.

Why Choosing the Right GPU is Crucial for AI Workloads

Choosing the right GPU for AI workloads is crucial to ensure optimal performance and efficiency. It can be a waste of time and resources if you try to run a large model without the right compute. Hyperstack’s GPU cloud services provide several factors that must be considered, including CUDA cores, architecture, memory capacity, bandwidth, multi-GPU scalability, price, and budget. By evaluating these factors carefully, we can determine the best GPU for any large language model.

Recommended GPUs for AI Model Training

If you visit Hyperstack’s website and go to the Solutions tab, you will find a page summarizing the GPUs suitable for AI model training. They recommend different GPUs for AI and AI model training, such as the A100, which supercharges inference with hundreds and achieves up to 30x acceleration with low latency. The H100 PLCE unlocks the potential of 800s for AI model training and offers advanced model analysis and accurate predictions. Lastly, the H100 SXM unlocks the potential of A100s for AI model training and provides advanced model analysis and accurate predictions. Hyperstack also offers other products suited for various AI use cases.

Calculating GPU Memory Requirements and Tokens

Calculating the GPU memory requirement and tokens for large language models is essential. The memory requirement depends on the model size (number of parameters) and the model’s precision. You also need to consider the batch size, tokenization schema, and other factors. There are online tools, such as Ru GPU Port, that can help you calculate the GPU memory requirement based on specific model parameters.

Host Large Language Models with Hyperstack

Hyperstack is a virtual machine that allows you to host any large language model using their cloud GPU service. You can choose the GPU flavor that suits your needs, such as the RTX A6000 or the A100. By deploying a new virtual machine and setting up your environment and SSH key, you can easily host your large language model on Hyperstack. They provide detailed instructions on their website and have a video tutorial to guide you through the process.

Conclusion

Choosing the right GPU for large language models is crucial for optimal performance and efficiency. Hyperstack’s GPU cloud services offer a range of GPUs suitable for AI model training. By calculating the GPU memory requirements and tokens, you can determine the best GPU for your specific model. Hosting your large language models on Hyperstack is simple and efficient, thanks to their virtual machine and cloud GPU service. Make sure to check out Hyperstack’s website for more information on recommended GPUs for AI and AI model training.

Thank you for reading this article. I hope you found it useful in understanding how to choose the right GPU for large language models. Remember to subscribe to our YouTube channel and follow us on social media for more AI-related content. Have a great day!