Today, we are excited 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 design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support discovering to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement knowing (RL) step, which was utilized to refine the model’s reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, bytes-the-dust.com meaning it’s equipped to break down complicated queries and reason through them in a detailed manner. This directed reasoning process allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market’s attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical reasoning and information interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient reasoning by routing inquiries to the most appropriate professional “clusters.” This approach permits the design to concentrate on various problem domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the of the main R1 design 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 effective designs to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and examine designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only 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 across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you’re utilizing 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 request a limitation boost, develop a limit boost demand and reach out 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) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and assess designs against crucial safety criteria. You can execute safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model responses 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 create the guardrail, see the GitHub repo.

The basic flow includes the following steps: 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 to the design for inference. After receiving the design’s output, another guardrail check is applied. 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, wavedream.wiki 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 show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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 utilize the InvokeModel API to invoke the design. It doesn’t support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
   
   The design detail page offers essential details about the model’s abilities, pricing structure, and execution guidelines. You can find detailed use instructions, including sample API calls and code bits for combination. The model supports numerous text generation jobs, including content development, code generation, and concern answering, using its support finding out optimization and CoT thinking abilities. The page also includes implementation choices and licensing details to help you start with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, choose Deploy.
   
   You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
3. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
4. For Number of instances, get in a variety of circumstances (in between 1-100).
5. For example type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to align with your organization’s security and compliance requirements.
6. Choose Deploy to start utilizing the model.
   
   When the implementation is total, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock play ground.
7. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change model specifications like temperature and optimum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat template for optimum outcomes. For instance, material for inference.
   
   This is an outstanding way to explore the design’s thinking and text generation capabilities before incorporating it into your applications. The play area supplies instant feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.
   
   You can quickly evaluate the model in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
   
   Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
   
   The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to generate text based on 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 designs to your usage case, with your data, and release them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s explore both techniques to assist you select the method that finest fits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, pick Studio in the navigation pane.
8. First-time users will be triggered to produce a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The model internet browser displays available designs, with details like the provider name and design abilities.
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card shows crucial details, consisting of:
   
   - Model name
10. Provider name
11. Task category (for instance, Text Generation). Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
    
    5. Choose the model 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 model. 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 deploy the design, it’s suggested to examine the design details and license terms to confirm compatibility with your usage case.
    
    6. Choose Deploy to continue with deployment.
    
    7. For Endpoint name, use the automatically created name or develop a custom-made one.
15. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
16. For Initial instance count, enter the number of circumstances (default: 1). Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
17. Review all configurations 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 deploy the model.
    
    The deployment process can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS consents 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 releasing the model is supplied 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
19. In the Managed implementations area, find the endpoint you wish to delete.
20. Select the endpoint, and on the Actions menu, choose Delete.
21. Verify the endpoint details to make certain you’re deleting the appropriate release: 1. Endpoint name.
22. Model name.
23. 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 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 start. 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 Getting started with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business construct ingenious solutions utilizing AWS services and sped up compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his leisure time, Vivek takes pleasure in treking, watching motion pictures, and trying different foods.
    
    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 a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group 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 solutions that assist customers accelerate their AI journey and unlock organization value.