Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI’s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing function is its reinforcement knowing (RL) action, which was used to improve the design’s responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it’s geared up to break down complicated inquiries and factor through them in a detailed manner. This assisted thinking process allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry’s attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, logical thinking and data interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most pertinent specialist “clusters.” This approach enables the model to concentrate on different problem domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to imitate the behavior and pediascape.science reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you’re using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit boost, create a limit boost demand and connect to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and evaluate models against essential security requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general flow involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent to the design for inference. After getting the design’s output, another guardrail check is used. If the output passes this final check, it’s returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
   
   The design detail page offers essential details about the model’s abilities, rates structure, and application guidelines. You can find detailed use directions, including sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of material development, code generation, and question answering, using its support finding out optimization and CoT thinking abilities. The page likewise consists of implementation choices and licensing details to help you get begun with DeepSeek-R1 in your applications.
2. To start utilizing DeepSeek-R1, select Deploy.
   
   You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
4. For Number of circumstances, enter a variety of instances (in between 1-100).
5. For example type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your organization’s security and compliance requirements.
6. Choose Deploy to start utilizing the design.
   
   When the release is total, you can check DeepSeek-R1’s capabilities straight in the Amazon Bedrock play area.
7. Choose Open in playground to access an interactive user interface where you can experiment with different prompts and change model criteria like temperature level and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimum outcomes. For example, content for inference.
   
   This is an outstanding way to check out the model’s reasoning and text generation capabilities before incorporating it into your applications. The playground offers immediate feedback, assisting you understand how the design reacts to various inputs and letting you tweak your triggers for optimum outcomes.
   
   You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
   
   The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invokemodel and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrockruntime customer, sets up inference specifications, and sends out a demand to generate text based on a user prompt.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s explore both methods to help you choose the method that finest suits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be triggered to produce a domain.
9. On the SageMaker Studio console, select JumpStart in the navigation pane.
   
   The model web browser shows available models, with details like the supplier name and model abilities.
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each model card reveals essential details, consisting of:
   
   - Model name
10. Provider name
11. Task classification (for example, Text Generation). Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
    
    5. Choose the model card to see the design details page.
    
    The design details page includes the following details:
    
    - The design name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
    
    The About tab includes important details, such as:
    
    - Model description.
12. License details. - Technical specs.
13. Usage guidelines
    
    Before you release the model, it’s suggested to evaluate the design details and license terms to validate compatibility with your use case.
    
    6. Choose Deploy to continue with implementation.
    
    7. For Endpoint name, utilize the instantly generated name or produce a custom one.
14. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
15. For Initial instance count, enter the variety of instances (default: 1). Selecting proper instance types and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
16. Review all configurations for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
17. Choose Deploy to deploy the model.
    
    The deployment process can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the model using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Clean up
    
    To charges, complete the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the design using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
18. In the Managed implementations section, find the endpoint you wish to delete.
19. Select the endpoint, and on the Actions menu, pick Delete.
20. Verify the endpoint details to make certain you’re erasing the proper deployment: 1. Endpoint name.
21. Model name.
22. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his spare time, Vivek enjoys treking, viewing movies, and trying various foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is an Expert Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about developing solutions that help consumers accelerate their AI journey and unlock organization worth.