Today, we are delighted 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 first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, 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 large language model (LLM) established by DeepSeek AI that uses support finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its support knowing (RL) action, which was used to fine-tune the design’s reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it’s geared up to break down complex questions and reason through them in a detailed manner. This directed reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry’s attention as a flexible text-generation design that can be integrated into various workflows such as agents, sensible reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, making it possible for effective inference by routing queries to the most pertinent professional “clusters.” This technique permits the model to focus on various problem domains while maintaining total efficiency. 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 circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model 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 sized, more efficient models to imitate the behavior 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 design, we suggest deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate models against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release 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, choose 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 instance in the AWS Region you are deploying. To request a limitation boost, create a limitation increase request and connect to your account group.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging content, and assess designs against essential safety requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model responses 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 develop the guardrail, see the GitHub repo.

The general circulation involves the following actions: 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 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 outcome. 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 happened at the input or output phase. The examples showcased in the following areas 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 structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can use 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 pick the DeepSeek-R1 design.
   
   The design detail page provides essential details about the model’s abilities, rates structure, and application guidelines. You can discover detailed use directions, including sample API calls and code bits for integration. The model supports different text generation jobs, including material development, code generation, and concern answering, using its support learning optimization and CoT thinking abilities. The page likewise consists of implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, pick Deploy.
   
   You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
3. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
4. For Variety of circumstances, get in a number of instances (between 1-100).
5. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might want to review these settings to align with your company’s security and wiki.myamens.com compliance requirements.
6. Choose Deploy to start utilizing the design.
   
   When the deployment is complete, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
7. Choose Open in play ground to access an interactive interface where you can explore different triggers and adjust design criteria like temperature level and maximum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimal results. For example, material for reasoning.
   
   This is an excellent method to explore the design’s thinking and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimal results.
   
   You can rapidly evaluate the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example shows how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends 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, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s check out both approaches to help you pick the method that finest suits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be triggered to create a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The model internet browser shows available designs, with details like the supplier name and .
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each model card shows key details, including:
   
   - Model name
10. Provider name
11. Task classification (for pediascape.science example, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
    
    5. Choose the design card to see the model details page.
    
    The model details page consists of the following details:
    
    - The model name and supplier details. Deploy button to deploy 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 requirements.
14. Usage guidelines
    
    Before you release the model, it’s advised to review the model details and license terms to validate compatibility with your usage case.
    
    6. Choose Deploy to continue with release.
    
    7. For Endpoint name, use the automatically generated name or create a custom-made one.
15. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial circumstances count, go into the number of circumstances (default: 1). Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your implementation to change 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 setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to deploy the model.
    
    The release process can take numerous minutes to finish.
    
    When release is total, your endpoint status will change to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed 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 notebook and range from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use 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 shown in the following code:
    
    Clean up
    
    To avoid unwanted charges, finish the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
19. In the Managed releases area, locate the endpoint you want to erase.
20. Select the endpoint, and on the Actions menu, select Delete.
21. Verify the endpoint details to make certain you’re deleting the correct implementation: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design 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 explored 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 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 started with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build innovative options using AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his spare time, Vivek takes pleasure in hiking, seeing motion pictures, and attempting various 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 Science and Bioinformatics.
    
    Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about constructing options that help customers accelerate their AI journey and unlock business value.