Today, we are delighted to reveal 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 design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement learning to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its reinforcement knowing (RL) step, which was used to improve the model’s reactions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it’s geared up to break down complicated queries and factor through them in a detailed way. This assisted thinking procedure allows the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the market’s attention as a versatile text-generation design that can be incorporated into various workflows such as agents, sensible thinking and data interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient inference by routing inquiries to the most relevant professional “clusters.” This method allows the model to specialize in different issue domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine designs against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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 releasing. To ask for a limitation boost, develop a limitation increase demand and reach out to your account team.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and examine designs against key security requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic circulation involves the following actions: First, the system receives 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 reasoning. After getting the model’s output, another guardrail check is used. If the output passes this last check, it’s returned as the last outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
   
   The model detail page offers important details about the model’s abilities, pricing structure, and application guidelines. You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The model supports different text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning capabilities. The page also includes implementation options and licensing details to assist you get going with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, select Deploy.
   
   You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, 89u89.com go into an endpoint name (between 1-50 alphanumeric characters).
4. For Number of circumstances, go into a variety of instances (in between 1-100).
5. For example type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your organization’s security and compliance requirements.
6. Choose Deploy to begin utilizing the model.
   
   When the implementation is complete, you can test DeepSeek-R1’s capabilities straight in the Amazon Bedrock playground.
7. Choose Open in play ground to access an interactive interface where you can explore different triggers and change model parameters like temperature level and maximum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for ideal results. For example, content for inference.
   
   This is an excellent way to explore the design’s thinking and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for ideal results.
   
   You can rapidly test the model in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a demand to create text based upon a user prompt.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production utilizing either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both methods to assist you select the method that best matches your needs.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be prompted to produce a domain.
9. On the SageMaker Studio console, choose JumpStart in the navigation pane.
   
   The design browser shows available designs, with details like the supplier name and model abilities.
   
   4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. Each model card shows key details, consisting of:
   
   - Model name
10. Provider name
11. Task category (for example, Text Generation). Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
    
    5. Choose the design card to view the model details page.
    
    The design details page consists of the following details:
    
    - The design name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
    
    The About tab includes important details, such as:
    
    - Model description.
12. License details.
13. Technical requirements.
14. Usage standards
    
    Before you release the model, it’s recommended to examine the model details and license terms to verify compatibility with your use case.
    
    6. Choose Deploy to continue with implementation.
    
    7. For Endpoint name, utilize the automatically generated name or develop a custom one.
15. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
16. For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate instance types and counts is vital for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
17. Review all configurations for accuracy. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to deploy the model.
    
    The deployment process can take several minutes to complete.
    
    When implementation is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Tidy up
    
    To prevent undesirable charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
19. In the Managed releases area, find the endpoint you desire to delete.
20. Select the endpoint, and on the Actions menu, select Delete.
21. Verify the endpoint details to make certain you’re erasing the proper deployment: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the predictor
    
    The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 companies build ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the inference performance of large language designs. In his spare time, Vivek delights in hiking, watching films, and attempting different cuisines.
    
    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 working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about building services that assist consumers accelerate their AI journey and unlock company value.