Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and surgiteams.com Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI’s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) step, which was utilized to fine-tune the model’s actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it’s equipped to break down intricate queries and reason through them in a detailed way. This guided reasoning process allows the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the market’s attention as a versatile text-generation design that can be integrated into various workflows such as representatives, logical thinking and information analysis tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most pertinent specialist “clusters.” This method enables the model to specialize in various issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance 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 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 refers to a procedure of training smaller sized, more effective designs to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, 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 validate 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 deploying. To ask for a limitation boost, create a limit boost demand and connect to your account team.

Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous content, and examine designs against crucial safety requirements. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic circulation involves the following actions: 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 model for inference. 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 indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, choose Model brochure under Foundation models 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 company and pick the DeepSeek-R1 model.
   
   The model detail page offers essential details about the design’s abilities, pricing structure, and application standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports different text generation jobs, consisting of content creation, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. The page also includes deployment choices and licensing details to help you get begun with DeepSeek-R1 in your applications.
2. To begin utilizing DeepSeek-R1, pick Deploy.
   
   You will be prompted to set up the release 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 Number of circumstances, enter a number of instances (in between 1-100).
5. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your organization’s security and compliance requirements.
6. Choose Deploy to begin using the model.
   
   When the implementation is complete, you can check DeepSeek-R1’s capabilities straight in the Amazon Bedrock play ground.
7. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust design parameters like temperature level and maximum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimum results. For example, material for inference.
   
   This is an exceptional method to check out the model’s thinking and text generation abilities before incorporating it into your applications. The play area supplies instant feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your triggers for optimal results.
   
   You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
   
   Run reasoning 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 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 develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a request to produce text based on 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 solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can models to your usage case, with your information, and deploy them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both approaches to help you select the approach that best suits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following actions to release DeepSeek-R1 utilizing 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, pick JumpStart in the navigation pane.
   
   The design internet browser shows available designs, with details like the company name and model capabilities.
   
   4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. Each design card shows essential details, consisting of:
   
   - Model name
10. Provider name
11. Task category (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
    
    5. Choose the design card to view the model details page.
    
    The design details page includes the following details:
    
    - The design name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
    
    The About tab consists of essential details, such as:
    
    - Model description.
12. License details.
13. Technical specifications.
14. Usage guidelines
    
    Before you release the design, it’s recommended to evaluate the model details and license terms to validate compatibility with your usage case.
    
    6. Choose Deploy to continue with release.
    
    7. For Endpoint name, utilize the immediately created name or produce a custom one.
15. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to adjust 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 setups for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
18. Choose Deploy to release the model.
    
    The implementation process can take a number of minutes to complete.
    
    When deployment is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime client 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 install the SageMaker Python SDK and make certain you have the needed AWS approvals 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 deploying the design is supplied 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 likewise 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 displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    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, pick Marketplace implementations.
19. In the Managed deployments area, find the endpoint you want 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 deployment: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design 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 release the DeepSeek-R1 design 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 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 construct innovative solutions utilizing AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his spare time, Vivek takes pleasure in hiking, seeing movies, and attempting various foods.
    
    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 a Specialist Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about building solutions that help consumers accelerate their AI journey and unlock company value.