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Introduction
The world of Artificial Intelligence (AI) has witnessed a significant boost with the introduction of specialized hardware designed specifically for machine learning tasks. Google’s custom-made AI hardware, TPU (Tensor Processing Unit), has been at the forefront of this development, enabling faster and more cost-effective processing of AI workloads. In a collaborative effort, Hugging Face and Google have joined forces to bring the performance and efficiency of TPUs to Hugging Face Inference Endpoints and Spaces.
Hugging Face Inference Endpoints Support for TPUs
We’re thrilled to announce that AI builders can now accelerate their applications with Google Cloud TPUs on Hugging Face Inference Endpoints and Spaces!
For those who may not be familiar, TPUs are custom-made AI hardware designed by Google to deliver impressive performance across various AI workloads. This collaboration has resulted in the integration of TPUs into Hugging Face Inference Endpoints, providing developers with a seamless way to deploy Generative AI models on a dedicated, managed infrastructure using the cloud provider of their choice.
Choose the Model You Want to Deploy
Starting today, Google TPU v5e is available on Inference Endpoints. Simply select the model you want to deploy, choose Google Cloud Platform, select us-west1, and select a TPU configuration:
- v5litepod-1 TPU v5e with 1 core and 16 GB memory ($1.375/hour)
- v5litepod-4 TPU v5e with 4 cores and 64 GB memory ($5.50/hour)
- v5litepod-8 TPU v5e with 8 cores and 128 GB memory ($11.00/hour)
Tips for Choosing the Right TPU Configuration
We recommend using v5litepod-4 for larger models to avoid memory budget issues. The larger the configuration, the lower the latency will be. You can use v5litepod-1 for models with up to 2 billion parameters without much hassle.
Hugging Face Spaces Support for TPUs
Hugging Face Spaces provide developers with a platform to create, deploy, and share AI-powered demos and applications quickly. We are excited to introduce new TPU v5e instance support for Hugging Face Spaces.
To upgrade your Space to run on TPUs, navigate to the Settings button in your Space and select the desired configuration:
- v5litepod-1 TPU v5e with 1 core and 16 GB memory ($1.375/hour)
- v5litepod-4 TPU v5e with 4 cores and 64 GB memory ($5.50/hour)
- v5litepod-8 TPU v5e with 8 cores and 128 GB memory ($11.00/hour)
Conclusion
In conclusion, the collaboration between Hugging Face and Google has resulted in a powerful combination of TPUs and Inference Endpoints, enabling developers to accelerate their AI applications with improved performance and cost efficiency. This integration has also resulted in the creation of an open-source library called Optimum TPU, which makes it super easy for developers to train and deploy Hugging Face models on Google TPUs.
Frequently Asked Questions
Question 1: What are TPUs?
TPUs (Tensor Processing Units) are custom-made AI hardware designed by Google to deliver impressive performance across various AI workloads.
Question 2: How do I choose the right TPU configuration?
We recommend using v5litepod-4 for larger models to avoid memory budget issues. The larger the configuration, the lower the latency will be. You can use v5litepod-1 for models with up to 2 billion parameters without much hassle.
Question 3: What is the cost of using TPUs?
The cost of using TPUs varies depending on the configuration. v5litepod-1 costs $1.375/hour, v5litepod-4 costs $5.50/hour, and v5litepod-8 costs $11.00/hour.
Question 4: What models are supported by Optimum TPU?
Optimum TPU supports Hugging Face models, including Gemma, Llama, and Mistral.
Question 5: Can I deploy my models on TPUs using Inference Endpoints?
Yes, you can deploy your models on TPUs using Inference Endpoints. Simply select the model you want to deploy, choose Google Cloud Platform, select us-west1, and select a TPU configuration.
