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 deploy DeepSeek AI‘s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement knowing (RL) action, which was used to refine the model’s actions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it’s equipped to break down intricate queries and factor through them in a detailed manner. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market’s attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, sensible reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most pertinent expert “clusters.” This approach allows the model to focus on various problem domains while maintaining total effectiveness. 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based on 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 habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and examine models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, 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, 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 limit boost, develop a limit 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 proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and assess models against crucial security criteria. You can carry out safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic circulation includes the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent to the model for inference. After getting the design’s output, forum.pinoo.com.tr another guardrail check is applied. If the output passes this final check, it’s returned as the 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 inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 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 use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.

The design detail page supplies important details about the model’s capabilities, rates structure, and execution standards. 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 content production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning abilities. The page also includes implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be . 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, go into a variety of instances (in between 1-100). 6. For example type, pick your circumstances type. For ideal performance 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 role approvals, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might want to examine these settings to align with your company’s security and compliance requirements. 7. Choose Deploy to begin using the model.

When the implementation is complete, you can test DeepSeek-R1’s capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive user interface where you can experiment with different triggers and change model parameters like temperature level and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for optimum results. For instance, content for reasoning.

This is an outstanding method to check out the model’s reasoning and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the design responds to numerous inputs and letting you fine-tune your triggers for optimum results.

You can quickly test the design in the play area through the UI. However, to invoke the released 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 shows how to carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends out a request to generate 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 services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s check out both techniques to help you select the technique 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. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model browser shows available designs, with details like the service provider name and model abilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. Each design card reveals key details, consisting of:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
  
  5. Choose the model card to see the model details page.
  
  The design details page includes the following details:
  
  - The design name and supplier details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you deploy the model, it’s suggested to review the model details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the immediately created name or create a custom one.

8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of instances (default: 1). Selecting proper instance types and counts is vital for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the design.
    
    The deployment process can take several minutes to complete.
    
    When release is total, your endpoint status will change to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and range 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
    
    Clean up
    
    To prevent undesirable charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model using Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
12. In the Managed releases area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you’re deleting the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we checked out 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 models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies construct innovative services utilizing AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning performance of large language models. In his totally free time, Vivek enjoys treking, enjoying films, and trying various cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about developing options that assist customers accelerate their AI journey and unlock company value.