Today, we are thrilled to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI’s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and properly 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 variations of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) action, which was utilized to refine the model’s responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it’s equipped to break down intricate inquiries and factor through them in a detailed manner. This assisted thinking procedure enables the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market’s attention as a versatile text-generation model that can be integrated into different workflows such as agents, rational thinking and information analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient reasoning by routing questions to the most relevant expert “clusters.” This method enables the design to concentrate on different problem 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 use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models 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 refers to a process of training smaller, more efficient designs to imitate the behavior and thinking patterns of the bigger 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 design, we suggest deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate designs against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 circumstances in the AWS Region you are releasing. To ask for a limitation increase, produce a limit increase demand and connect to your account group.

Because you will be releasing this design 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 guidelines, see Establish authorizations to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful material, and examine designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic flow includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent out to the design for inference. After getting the design’s output, another guardrail check is used. If the output passes this last check, it’s returned as the last result. 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 happened at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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

1. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
   
   The model detail page provides essential details about the design’s capabilities, prices structure, and implementation guidelines. You can discover detailed use directions, including sample API calls and code snippets for combination. The model supports different text generation jobs, consisting of material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. The page likewise includes implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
2. To start utilizing DeepSeek-R1, choose Deploy.
   
   You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
4. For Number of instances, enter a variety of instances (between 1-100).
5. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might desire to examine these settings to align with your company’s security and compliance requirements.
6. Choose Deploy to start using the design.
   
   When the implementation is complete, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play ground.
7. Choose Open in playground to access an interactive user interface where you can explore different prompts and adjust design criteria like temperature level and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for ideal outcomes. For instance, content for inference.
   
   This is an outstanding way to explore the model’s thinking and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you understand how the model responds to numerous inputs and letting you fine-tune your triggers for optimal outcomes.
   
   You can rapidly check the design in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
   
   Run inference using guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a demand to create text based on a user timely.
   
   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 simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s explore both methods to assist you select the technique that best suits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, pick Studio in the navigation pane.
8. First-time users will be prompted to produce a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The model browser displays available models, with details like the service provider name and model abilities.
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. Each model card shows key details, including:
   
   - Model name
10. Provider name
11. Task classification (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
    
    5. Choose the model card to see the model details page.
    
    The model details page consists of 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 consists of important details, such as:
    
    - Model description.
12. License details.
13. Technical specs.
14. Usage standards
    
    Before you release the design, it’s suggested to review the design details and license terms to verify compatibility with your usage case.
    
    6. Choose Deploy to continue with release.
    
    7. For Endpoint name, utilize the immediately created name or develop a custom one.
15. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial instance count, enter the number of instances (default: 1). Selecting appropriate circumstances types and counts is vital for bytes-the-dust.com cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
17. Review all configurations for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to release the design.
    
    The implementation process can take a number of minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise 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 displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
19. In the Managed releases area, find the endpoint you want to erase.
20. Select the endpoint, and on the Actions menu, pick Delete.
21. Verify the endpoint details to make certain you’re deleting the correct implementation: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain expenses 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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 innovative options using AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek enjoys hiking, seeing films, and trying different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, yewiki.org SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about building solutions that assist consumers accelerate their AI journey and unlock company worth.