Today, we are delighted 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 design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses support learning to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its reinforcement learning (RL) step, which was utilized to refine the model’s responses beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it’s geared up to break down complex queries and factor through them in a detailed way. This guided reasoning procedure enables the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry’s attention as a versatile text-generation model that can be incorporated into various workflows such as agents, sensible thinking and data analysis tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing inquiries to the most appropriate professional “clusters.” This technique enables the model to focus on different issue domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based on 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 designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, 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 location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate designs against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

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

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) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and examine models against essential safety requirements. You can execute security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic flow includes the following actions: First, pipewiki.org 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 out to the design for inference. After getting the model’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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:

1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. At the time of composing this post, you can use 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 supplier and select the DeepSeek-R1 model.
   
   The model detail page offers important details about the model’s abilities, pricing structure, and implementation guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports different text generation tasks, including material creation, code generation, and concern answering, using its support learning optimization and CoT reasoning capabilities. The page also includes release options and licensing details to help you get begun with DeepSeek-R1 in your applications.
2. To begin using DeepSeek-R1, pick Deploy.
   
   You will be triggered to configure 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 Number of circumstances, enter a variety of instances (between 1-100).
5. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For it-viking.ch most utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your company’s security and compliance requirements.
6. Choose Deploy to start utilizing the design.
   
   When the implementation is total, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
7. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust design criteria like temperature level and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimum results. For example, content for reasoning.
   
   This is an exceptional method to explore the model’s reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your prompts for ideal outcomes.
   
   You can quickly evaluate the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
   
   The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to generate text based upon a user timely.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with just 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 utilizing either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s check out both methods to help you pick the technique that finest matches your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, garagesale.es pick Studio in the navigation pane.
8. First-time users will be prompted to develop a domain.
9. On the SageMaker Studio console, choose JumpStart in the navigation pane.
   
   The design web browser shows available designs, with details like the provider name and model abilities.
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card reveals essential details, including:
   
   - Model name
10. Provider name
11. Task classification (for example, Text Generation). Bedrock Ready badge (if relevant), that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
    
    5. Choose the model card to view the model details page.
    
    The model details page includes the following details:
    
    - The design name and company details. Deploy button to deploy 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 requirements.
14. Usage guidelines
    
    Before you deploy the model, it’s advised to examine the model details and license terms to validate compatibility with your use case.
    
    6. Choose Deploy to continue with release.
    
    7. For Endpoint name, utilize the automatically generated name or create a custom one.
15. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial circumstances count, enter the variety of circumstances (default: 1). Selecting proper circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
17. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
18. Choose Deploy to release the design.
    
    The release process can take several minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use 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 revealed in the following code:
    
    Tidy up
    
    To prevent unwanted charges, finish the actions in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
19. In the Managed releases section, wiki.whenparked.com find 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 erasing the proper 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 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 Starting 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 services using AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of large language designs. In his downtime, Vivek takes pleasure in hiking, enjoying movies, 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 a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about constructing solutions that assist consumers accelerate their AI journey and unlock service value.