Today, we are thrilled to announce 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, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses support discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its support knowing (RL) step, which was utilized to fine-tune the model’s responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it’s geared up to break down intricate queries and reason through them in a detailed manner. This directed reasoning process allows the design to produce more accurate, transparent, and detailed answers. This design combines RL-based with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry’s attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, logical reasoning and data interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective inference by routing questions to the most pertinent specialist “clusters.” This technique enables the model to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance 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 thinking abilities of the main R1 design to more efficient 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 designs to simulate the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect 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 usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation increase, create a limitation boost request and connect to your account team.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful material, and assess models against essential security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

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 to the model for reasoning. After getting the design’s output, another guardrail check is applied. If the output passes this final check, it’s returned as the result. 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 occurred at the input or output phase. The examples showcased in the following areas 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 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, pick Model brochure under Foundation models in the navigation pane. At the time of composing this post, you can use the InvokeModel API to invoke the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
   
   The design detail page supplies vital details about the design’s capabilities, rates structure, and implementation standards. You can discover detailed use guidelines, including sample API calls and code bits for combination. The model supports different text generation jobs, including content development, code generation, and concern answering, using its support discovering optimization and CoT reasoning capabilities. The page likewise consists of deployment options and licensing details to assist you begin with DeepSeek-R1 in your applications.
2. To start using DeepSeek-R1, pick Deploy.
   
   You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
4. For Number of circumstances, get in a number of circumstances (between 1-100).
5. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role consents, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you may want to examine these settings to line up with your organization’s security and compliance requirements.
6. Choose Deploy to begin utilizing 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 play area to access an interactive interface where you can explore different prompts and adjust design specifications like temperature level and maximum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat template for optimum outcomes. For example, material for reasoning.
   
   This is an outstanding way to check out the design’s thinking and text generation abilities before integrating it into your applications. The play area provides immediate feedback, helping you comprehend how the design responds to different inputs and letting you fine-tune your prompts for ideal outcomes.
   
   You can quickly evaluate the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
   
   The following code example demonstrates 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 actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to generate text based upon a user timely.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a few 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 offers 2 practical approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s check out both techniques to help you select the technique that best suits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be triggered to develop a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The design internet browser displays available designs, with details like the provider name and model abilities.
   
   4. Search for DeepSeek-R1 to see the DeepSeek-R1 model 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 relevant), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
    
    5. Choose the design card to see the design details page.
    
    The design details page includes the following details:
    
    - The design name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
    
    The About tab includes crucial details, such as:
    
    - Model description.
12. License details.
13. Technical specs.
14. Usage guidelines
    
    Before you deploy the design, it’s advised to review the design details and license terms to validate compatibility with your use case.
    
    6. Choose Deploy to continue with implementation.
    
    7. For Endpoint name, use the automatically generated name or produce a custom-made one.
15. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial circumstances count, enter the number of circumstances (default: 1). Selecting proper circumstances types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
17. Review all setups for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
18. Choose Deploy to release the design.
    
    The deployment procedure can take numerous minutes to finish.
    
    When deployment is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise utilize 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 displayed in the following code:
    
    Clean up
    
    To avoid unwanted charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
19. In the Managed releases area, find the endpoint you desire to erase.
20. Select the endpoint, and on the Actions menu, choose Delete.
21. Verify the endpoint details to make certain you’re deleting the proper implementation: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain costs 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 checked out 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, wavedream.wiki describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 build ingenious services using AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek enjoys hiking, watching movies, and trying different cuisines.
    
    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 Science 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 tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is enthusiastic about developing options that help customers accelerate their AI journey and unlock company value.