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 frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to start 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 large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support learning (RL) action, which was used to refine the design’s reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it’s geared up to break down complicated questions and factor through them in a detailed manner. This directed thinking procedure permits the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry’s attention as a flexible text-generation model that can be integrated into various workflows such as agents, sensible reasoning and information interpretation tasks.

DeepSeek-R1 uses 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 inference by routing questions to the most appropriate specialist “clusters.” This technique permits the design to specialize in various problem domains while maintaining overall efficiency. 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 comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective 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, more effective models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, archmageriseswiki.com and examine designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, higgledy-piggledy.xyz and confirm 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 limitation boost, produce a limitation increase request and reach out 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) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and assess models against key security criteria. You can carry out security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions released 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 create the guardrail, see the GitHub repo.

The general circulation 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 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 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 phase. The examples showcased in the following areas show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (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 writing 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 provider and choose the DeepSeek-R1 model.
   
   The design detail page offers essential details about the model’s capabilities, rates structure, and application standards. You can find detailed use directions, including sample API calls and code snippets for integration. The design supports various text generation tasks, consisting of content creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking capabilities. The page likewise consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, select Deploy.
   
   You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
4. For Variety of instances, get in a variety of circumstances (between 1-100).
5. For Instance type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may want to review these settings to line up with your company’s security and compliance requirements.
6. Choose Deploy to begin using the design.
   
   When the deployment is complete, you can evaluate 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 various prompts and adjust design specifications like temperature and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimal results. For instance, content for inference.
   
   This is an outstanding method to explore the model’s reasoning and text generation abilities before integrating it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your prompts for ideal results.
   
   You can quickly test the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example shows how to carry out reasoning 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 produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a request to produce 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 usage case, with your data, and deploy them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s explore both techniques to help you select the method that best matches your needs.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
   
   1. On the SageMaker console, select Studio in the navigation pane.
8. First-time users will be triggered to develop a domain.
9. On the SageMaker Studio console, select JumpStart in the navigation pane.
   
   The design web browser displays available models, with details like the provider name and design capabilities.
   
   4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card reveals crucial details, consisting of:
   
   - Model name
10. Provider name
11. Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
    
    5. Choose the design card to view the design details page.
    
    The design details page includes the following details:
    
    - The model name and company details. Deploy button to release the design. 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 deploy the model, it’s recommended to review the design details and license terms to confirm compatibility with your use case.
    
    6. Choose Deploy to continue with release.
    
    7. For Endpoint name, utilize the immediately generated name or produce a custom-made one.
15. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
16. For Initial instance count, get in the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your implementation 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.
17. Review all setups for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to release the design.
    
    The release procedure can take numerous minutes to complete.
    
    When deployment is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 using 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 authorizations 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 provided in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra requests 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, finish the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
19. In the Managed deployments section, find the endpoint you desire to erase.
20. Select the endpoint, and on the Actions menu, pick Delete.
21. Verify the endpoint details to make certain you’re erasing the appropriate release: 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 delete the endpoint if you desire 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 design 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 designs, 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 services utilizing AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his free time, Vivek enjoys hiking, viewing films, and attempting various 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 Science and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about constructing solutions that help clients accelerate their AI journey and unlock company worth.