Today, we are thrilled 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 release DeepSeek AI’s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement 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 more efficiently to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it’s geared up to break down intricate inquiries and reason through them in a detailed manner. This assisted reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry’s attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for efficient reasoning by routing questions to the most relevant expert “clusters.” This technique permits the design to concentrate on various issue domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine models against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 releases 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, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, 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, select Amazon SageMaker, and higgledy-piggledy.xyz verify you’re utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation boost, produce a limitation boost demand and connect to your account team.

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) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and examine models against key security requirements. You can carry out precaution for the DeepSeek-R1 design using 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 utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic flow includes the following steps: 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 model for reasoning. After receiving the design’s output, another guardrail check is applied. If the output passes this final 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 suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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

1. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.
   
   The design detail page offers vital details about the design’s capabilities, rates structure, and garagesale.es application standards. You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The design supports various text generation tasks, including material creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning abilities. The page likewise includes release choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, choose Deploy.
   
   You will be triggered to set up the release 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 circumstances, get in a number of instances (between 1-100).
5. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to align with your company’s security and compliance requirements.
6. Choose Deploy to start using the design.
   
   When the implementation is total, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
7. Choose Open in playground to access an interactive interface where you can explore different triggers and adjust design parameters like temperature and maximum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimum outcomes. For instance, material for inference.
   
   This is an excellent way to check out the model’s thinking and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, assisting you comprehend how the model responds to various inputs and letting you fine-tune your triggers for optimal outcomes.
   
   You can rapidly check the model in the play ground 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 released DeepSeek-R1 endpoint
   
   The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, pipewiki.org use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a request to produce text based upon 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 release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s explore both approaches to help you choose the technique that best suits your needs.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, select Studio in the navigation pane.
8. First-time users will be prompted to develop a domain.
9. On the SageMaker Studio console, select JumpStart in the navigation pane.
   
   The model internet browser displays available models, with details like the supplier name and model capabilities.
   
   4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each model card shows crucial details, including:
   
   - Model name
10. Provider name
11. Task category (for example, Text Generation). Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
    
    5. Choose the design card to see the model details page.
    
    The model details page consists of the following details:
    
    - The design name and service 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 specifications.
14. Usage standards
    
    Before you release the model, it’s advised to review the model details and license terms to confirm compatibility with your usage case.
    
    6. Choose Deploy to proceed with implementation.
    
    7. For Endpoint name, utilize the automatically produced name or produce a customized one.
15. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
16. For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
17. Review all configurations for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to release the model.
    
    The implementation procedure can take numerous minutes to complete.
    
    When release is complete, 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 deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer 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 required AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
19. In the Managed deployments section, find the endpoint you desire to delete.
20. Select the endpoint, and on the Actions menu, pick Delete.
21. Verify the endpoint details to make certain you’re erasing the right implementation: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released will sustain costs 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 deploy the DeepSeek-R1 model utilizing 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 Getting going 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 ingenious options utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference performance of big language models. In his complimentary time, Vivek delights in hiking, viewing movies, and attempting different cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about developing services that assist customers accelerate their AI journey and unlock company worth.