How to deploy and fine-tune DeepSeek models on AWS

A running document to showcase how to deploy and fine-tune DeepSeek R1 models with Hugging Face on AWS.

What is DeepSeek-R1?

If you’ve ever struggled with a tough math problem, you know how useful it is to think a little longer and work through it carefully. OpenAI’s o1 model showed that when LLMs are trained to do the same—by using more compute during inference—they get significantly better at solving reasoning tasks like mathematics, coding, and logic.

However, the recipe behind OpenAI’s reasoning models has been a well kept secret. That is, until last week, when DeepSeek released their DeepSeek-R1 model and promptly broke the internet (and the stock market!).

DeepSeek AI open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen architectures. You can find them all in the DeepSeek R1 collection.

We collaborate with Amazon Web Services to make it easier for developers to deploy the latest Hugging Face models on AWS services to build better generative AI applications.

Let’s review how you can deploy and fine-tune DeepSeek R1 models with Hugging Face on AWS.

• Deploy DeepSeek R1 models
  • Deploy on AWS with Hugging Face Inference Endpoints
  • [Deploy on Amazon Bedrock Marketplace]
  • Deploy on Amazon SageMaker AI with Hugging Face LLM DLCs
    • DeepSeek R1 on GPUs
    • Distilled models on GPUs
    • Distilled models on Neuron
  • Deploy on EC2 Neuron with the Hugging Face Neuron Deep Learning AMI
• Fine-tune DeepSeek R1 models
  • Fine tune on Amazon SageMaker AI with Hugging Face Training DLCs
  • Fine tune on EC2 Neuron with the Hugging Face Neuron Deep Learning AMI

Deploy DeepSeek R1 models

Deploy on AWS with Hugging Face Inference Endpoints

You can find DeepSeek R1 and distilled models, as well as other popular open LLMs, ready to deploy on optimized configurations in the Inference Endpoints Model Catalog.

| Note: The team is working on enabling DeepSeek models deployment on Inferentia instances. Stay tuned!