|
Throughout this previous AWS re:Invent, Amazon CEO Andy Jassy shared priceless classes realized from Amazon’s personal expertise growing practically 1,000 generative AI functions throughout the corporate. Drawing from this in depth scale of AI deployment, Jassy supplied three key observations which have formed Amazon’s method to enterprise AI implementation.
First is that as you get to scale in generative AI functions, the price of compute actually issues. Individuals are very hungry for higher worth efficiency. The second is definitely fairly troublesome to construct a extremely good generative AI software. The third is the variety of the fashions getting used once we gave our builders freedom to select what they need to do. It doesn’t shock us, as a result of we continue to learn the identical lesson over and again and again, which is that there’s by no means going to be one instrument to rule the world.
As Andy emphasised, a broad and deep vary of fashions supplied by Amazon empowers clients to decide on the exact capabilities that finest serve their distinctive wants. By carefully monitoring each buyer wants and technological developments, AWS recurrently expands our curated choice of fashions to incorporate promising new fashions alongside established {industry} favorites. This ongoing growth of high-performing and differentiated mannequin choices helps clients keep on the forefront of AI innovation.
This leads us to Chinese language AI startup DeepSeek. DeepSeek launched DeepSeek-V3 on December 2024 and subsequently launched DeepSeek-R1, DeepSeek-R1-Zero with 671 billion parameters, and DeepSeek-R1-Distill fashions starting from 1.5–70 billion parameters on January 20, 2025. They added their vision-based Janus-Professional-7B mannequin on January 27, 2025. The fashions are publicly out there and are reportedly 90-95% extra inexpensive and cost-effective than comparable fashions. Per Deepseek, their mannequin stands out for its reasoning capabilities, achieved by progressive coaching methods corresponding to reinforcement studying.
As we speak, now you can deploy DeepSeek-R1 fashions in Amazon Bedrock and Amazon SageMaker AI. Amazon Bedrock is finest for groups looking for to shortly combine pre-trained basis fashions by APIs. Amazon SageMaker AI is right for organizations that need superior customization, coaching, and deployment, with entry to the underlying infrastructure. Moreover, you can too use AWS Trainium and AWS Inferentia to deploy DeepSeek-R1-Distill fashions cost-effectively by way of Amazon Elastic Compute Cloud (Amazon EC2) or Amazon SageMaker AI.
With AWS, you should use DeepSeek-R1 fashions to construct, experiment, and responsibly scale your generative AI concepts through the use of this highly effective, cost-efficient mannequin with minimal infrastructure funding. You may also confidently drive generative AI innovation by constructing on AWS companies which are uniquely designed for safety. We extremely advocate integrating your deployments of the DeepSeek-R1 fashions with Amazon Bedrock Guardrails so as to add a layer of safety on your generative AI functions, which can be utilized by each Amazon Bedrock and Amazon SageMaker AI clients.
You possibly can select easy methods to deploy DeepSeek-R1 fashions on AWS at the moment in just a few methods: 1/ Amazon Bedrock Market for the DeepSeek-R1 mannequin, 2/ Amazon SageMaker JumpStart for the DeepSeek-R1 mannequin, 3/ Amazon Bedrock Custom Mannequin Import for the DeepSeek-R1-Distill fashions, and 4/ Amazon EC2 Trn1 cases for the DeepSeek-R1-Distill fashions.
Let me stroll you thru the varied paths for getting began with DeepSeek-R1 fashions on AWS. Whether or not you’re constructing your first AI software or scaling present options, these strategies present versatile beginning factors primarily based in your workforce’s experience and necessities.
1. The DeepSeek-R1 mannequin in Amazon Bedrock Market
Amazon Bedrock Market gives over 100 in style, rising, and specialised FMs alongside the present choice of industry-leading fashions in Amazon Bedrock. You possibly can simply uncover fashions in a single catalog, subscribe to the mannequin, after which deploy the mannequin on managed endpoints.
To entry the DeepSeek-R1 mannequin in Amazon Bedrock Market, go to the Amazon Bedrock console and choose Mannequin catalog beneath the Basis fashions part. You possibly can shortly discover DeepSeek by looking out or filtering by mannequin suppliers.
After testing the mannequin element web page together with the mannequin’s capabilities, and implementation pointers, you possibly can instantly deploy the mannequin by offering an endpoint title, selecting the variety of cases, and deciding on an occasion sort.
You may also configure superior choices that allow you to customise the safety and infrastructure settings for the DeepSeek-R1 mannequin together with VPC networking, service position permissions, and encryption settings. For manufacturing deployments, it’s best to evaluation these settings to align together with your group’s safety and compliance necessities.
With Amazon Bedrock Guardrails, you possibly can independently consider person inputs and mannequin outputs. You possibly can management the interplay between customers and DeepSeek-R1 together with your outlined set of insurance policies by filtering undesirable and dangerous content material in generative AI functions. The DeepSeek-R1 mannequin in Amazon Bedrock Market can solely be used with Bedrock’s ApplyGuardrail API to guage person inputs and mannequin responses for customized and third-party FMs out there exterior of Amazon Bedrock. To be taught extra, learn Implement model-independent security measures with Amazon Bedrock Guardrails.
Amazon Bedrock Guardrails can be built-in with different Bedrock instruments together with Amazon Bedrock Brokers and Amazon Bedrock Data Bases to construct safer and safer generative AI functions aligned with accountable AI insurance policies. To be taught extra, go to the AWS Accountable AI web page.
When utilizing DeepSeek-R1 mannequin with Bedrock’s InvokeModel API and the Playground Console, please use DeepSeek’s chat template for optimum outcomes. For instance, <｜start▁of▁sentence｜><｜Person｜>content material for inference<｜Assistant｜>.
Check with this step-by-step information on easy methods to deploy the DeepSeek-R1 mannequin in Amazon Bedrock Market. To be taught extra, go to Deploy fashions in Amazon Bedrock Market.
2. The DeepSeek-R1 mannequin in Amazon SageMaker JumpStart
Amazon SageMaker JumpStart is a machine studying (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you could deploy with only a few clicks. To deploy DeepSeek-R1 in SageMaker JumpStart, you possibly can uncover the DeepSeek-R1 mannequin in SageMaker Unified Studio, SageMaker Studio, SageMaker AI console, or programmatically by the SageMaker Python SDK.
Within the Amazon SageMaker AI console, open SageMaker Unified Studio or SageMaker Studio. In case of SageMaker Studio, select JumpStart and seek for “DeepSeek-R1” within the All public fashions web page.
You possibly can choose the mannequin and select deploy to create an endpoint with default settings. When the endpoint comes InService, you can also make inferences by sending requests to its endpoint.
You possibly can derive mannequin efficiency and ML operations controls with Amazon SageMaker AI options corresponding to Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The mannequin is deployed in an AWS safe atmosphere and beneath your digital non-public cloud (VPC) controls, serving to to help knowledge safety.
As like Bedrock Marketpalce, you should use the ApplyGuardrail API within the SageMaker JumpStart to decouple safeguards on your generative AI functions from the DeepSeek-R1 mannequin. Now you can use guardrails with out invoking FMs, which opens the door to extra integration of standardized and totally examined enterprise safeguards to your software circulation whatever the fashions used.
Check with this step-by-step information on easy methods to deploy DeepSeek-R1 in Amazon SageMaker JumpStart. To be taught extra, go to Uncover SageMaker JumpStart fashions in SageMaker Unified Studio or Deploy SageMaker JumpStart fashions in SageMaker Studio.
3. DeepSeek-R1-Distill fashions utilizing Amazon Bedrock Customized Mannequin Import
Amazon Bedrock Customized Mannequin Import gives the power to import and use your custom-made fashions alongside present FMs by a single serverless, unified API with out the necessity to handle underlying infrastructure. With Amazon Bedrock Customized Mannequin Import, you possibly can import DeepSeek-R1-Distill Llama fashions starting from 1.5–70 billion parameters. As I highlighted in my weblog submit about Amazon Bedrock Mannequin Distillation, the distillation course of includes coaching smaller, extra environment friendly fashions to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 mannequin with 671 billion parameters through the use of it as a instructor mannequin.
After storing these publicly out there fashions in an Amazon Easy Storage Service (Amazon S3) bucket or an Amazon SageMaker Mannequin Registry, go to Imported fashions beneath Basis fashions within the Amazon Bedrock console and import and deploy them in a completely managed and serverless atmosphere by Amazon Bedrock. This serverless method eliminates the necessity for infrastructure administration whereas offering enterprise-grade safety and scalability.
Check with this step-by-step information on easy methods to deploy DeepSeek-R1 fashions utilizing Amazon Bedrock Customized Mannequin Import. To be taught extra, go to Import a custom-made mannequin into Amazon Bedrock.
4. DeepSeek-R1-Distill fashions utilizing AWS Trainium and AWS Inferentia
AWS Deep Studying AMIs (DLAMI) gives custom-made machine pictures that you should use for deep studying in quite a lot of Amazon EC2 cases, from a small CPU-only occasion to the newest high-powered multi-GPU cases. You possibly can deploy the DeepSeek-R1-Distill fashions on AWS Trainuim1 or AWS Inferentia2 cases to get the most effective price-performance.
To get began, go to Amazon EC2 console and launch a trn1.32xlarge EC2 occasion with the Neuron Multi Framework DLAMI referred to as Deep Studying AMI Neuron (Ubuntu 22.04).
After getting related to your launched ec2 occasion, set up vLLM, an open-source instrument to serve Giant Language Fashions (LLMs) and obtain the DeepSeek-R1-Distill mannequin from Hugging Face. You possibly can deploy the mannequin utilizing vLLM and invoke the mannequin server.
To be taught extra, consult with this step-by-step information on easy methods to deploy DeepSeek-R1-Distill Llama fashions on AWS Inferentia and Trainium.
You may also go to the DeepSeek-R1-Distill-Llama-8B or deepseek-ai/DeepSeek-R1-Distill-Llama-70B mannequin playing cards on Hugging Face. Select Deploy after which Amazon SageMaker. From the AWS Inferentia and Trainium tab, copy the instance code for deploy DeepSeek-R1-Distill Llama fashions.
For the reason that launch of DeepSeek-R1, varied guides of its deployment for Amazon EC2 and Amazon Elastic Kubernetes Service (Amazon EKS) have been posted. Right here is a few further materials so that you can take a look at:
Issues to know
Listed below are just a few vital issues to know.
- Pricing – For publicly out there fashions like DeepSeek-R1, you might be charged solely the infrastructure worth primarily based on inference occasion hours you choose for Amazon Bedrock Markeplace, Amazon SageMaker JumpStart, and Amazon EC2. For the Bedrock Customized Mannequin Import, you might be solely charged for mannequin inference, primarily based on the variety of copies of your customized mannequin is energetic, billed in 5-minute home windows. To be taught extra, take a look at the Amazon Bedrock Pricing, Amazon SageMaker AI Pricing, and Amazon EC2 Pricing pages.
- Knowledge safety – You should utilize enterprise-grade security measures in Amazon Bedrock and Amazon SageMaker that can assist you make your knowledge and functions safe and personal. This implies your knowledge is just not shared with mannequin suppliers, and isn’t used to enhance the fashions. This is applicable to all fashions—proprietary and publicly out there—like DeepSeek-R1 fashions on Amazon Bedrock and Amazon SageMaker. To be taught extra, go to Amazon Bedrock Safety and Privateness and Safety in Amazon SageMaker AI.
Now out there
DeepSeek-R1 is usually out there at the moment in Amazon Bedrock Market and Amazon SageMaker JumpStart. You may also use DeepSeek-R1-Distill fashions utilizing Amazon Bedrock Customized Mannequin Import and Amazon EC2 cases with AWS Trainum and Inferentia chips.
Give DeepSeek-R1 fashions a strive at the moment within the Amazon Bedrock console, Amazon SageMaker AI console, and Amazon EC2 console, and ship suggestions to AWS re:Publish for Amazon Bedrock and AWS re:Publish for SageMaker AI or by your ordinary AWS Assist contacts.
— Channy
