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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Today, we are thrilled 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 release DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly 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 versions of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses reinforcement finding out to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) step, which was used to fine-tune the design’s responses beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it’s equipped to break down complicated queries and factor through them in a detailed manner. This directed reasoning procedure enables the design to more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry’s attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, logical reasoning and data analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, making it possible for effective inference by routing queries to the most relevant specialist “clusters.” This method permits the model to concentrate on various problem 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 model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more effective 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 sized, more efficient models to simulate the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against essential security 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 multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy 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, select 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 instance in the AWS Region you are releasing. To ask for a limitation increase, develop a limit boost demand and reach out to your account group.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and examine models against essential security criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model reactions 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 create the guardrail, see the GitHub repo.

The basic 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 out to the design for inference. After getting the design’s output, another guardrail check is applied. If the output passes this final check, it’s returned as the last outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 brochure under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.

The model detail page provides important details about the design’s abilities, rates structure, and application standards. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for integration. The model supports different text generation jobs, including material development, code generation, and question answering, using its support discovering optimization and CoT reasoning capabilities.
The page likewise includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.

You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number of instances (in between 1-100).
6. For Instance type, engel-und-waisen.de select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might desire to review these settings to line up with your organization’s security and compliance requirements.
7. Choose Deploy to start using the design.

When the release is complete, you can check DeepSeek-R1’s capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can experiment with different triggers and adjust design specifications like temperature and maximum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for ideal results. For instance, content for reasoning.

This is an excellent method to explore the design’s reasoning and text generation abilities before incorporating it into your applications. The play area offers instant feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimal results.

You can quickly evaluate the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference using a released DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends a request to create 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, pipewiki.org and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both methods to assist you choose the technique that finest suits your needs.

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.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The design browser displays available designs, with details like the provider name and design capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card reveals key details, consisting of:

– Model name
– Provider name
– Task classification (for example, larsaluarna.se Text Generation).
Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design

5. Choose the design card to see the design details page.

The model details page includes the following details:

– The model name and supplier details.
Deploy button to release the design.
About and Notebooks tabs with detailed details

The About tab consists of important details, such as:

– Model description.
– License details.
– Technical specs.
– Usage guidelines

Before you release the model, it’s suggested to examine the design details and license terms to verify compatibility with your usage case.

6. Choose Deploy to continue with implementation.

7. For Endpoint name, use the automatically created name or create a customized one.
8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of instances (default: 1).
Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.

The release procedure can take numerous minutes to complete.

When release is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 deploying the design is offered in the Github here. You can clone the notebook and range 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:

Tidy up

To prevent unwanted charges, complete the actions in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
2. In the Managed deployments section, locate the endpoint you want to delete.
3. Select the endpoint, and systemcheck-wiki.de on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re erasing the proper implementation: 1. Endpoint name.
2. Model name.
3. 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 erase the endpoint if you wish 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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 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 companies develop innovative services utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his spare time, Vivek enjoys treking, viewing films, and attempting different cuisines.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 an Expert Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about building options that help customers accelerate their AI journey and unlock organization value.