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Throughout this previous AWS re:Invent, Amazon CEO Andy Jassy shared invaluable classes realized from Amazon’s personal expertise creating practically 1,000 generative AI purposes throughout the corporate. Drawing from this in depth scale of AI deployment, Jassy provided three key observations which have formed Amazon’s strategy to enterprise AI implementation.
First is that as you get to scale in generative AI purposes, the price of compute actually issues. Persons are very hungry for higher value efficiency. The second is definitely fairly troublesome to construct a very good generative AI utility. The third is the range of the fashions getting used after we gave our builders freedom to select what they wish to do. It doesn’t shock us, as a result of we continue to learn the identical lesson over and time and again, which is that there’s by no means going to be one software to rule the world.
As Andy emphasised, a broad and deep vary of fashions supplied by Amazon empowers prospects to decide on the exact capabilities that finest serve their distinctive wants. By intently monitoring each buyer wants and technological developments, AWS repeatedly expands our curated collection of fashions to incorporate promising new fashions alongside established {industry} favorites. This ongoing enlargement of high-performing and differentiated mannequin choices helps prospects 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 reasonably priced and cost-effective than comparable fashions. Per Deepseek, their mannequin stands out for its reasoning capabilities, achieved via revolutionary coaching methods resembling 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 via APIs. Amazon SageMaker AI is right for organizations that need superior customization, coaching, and deployment, with entry to the underlying infrastructure. Moreover, you can even 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 utilize 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. It’s also possible to confidently drive generative AI innovation by constructing on AWS providers which might be 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 to your generative AI purposes, which can be utilized by each Amazon Bedrock and Amazon SageMaker AI prospects.
You may select the best way to deploy DeepSeek-R1 fashions on AWS at this time in a number of 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 utility or scaling current options, these strategies present versatile beginning factors primarily based in your crew’s experience and necessities.
1. The DeepSeek-R1 mannequin in Amazon Bedrock Market
Amazon Bedrock Market presents over 100 standard, rising, and specialised FMs alongside the present collection of industry-leading fashions in Amazon Bedrock. You may 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 below the Basis fashions part. You may shortly discover DeepSeek by looking or filtering by mannequin suppliers.
After testing the mannequin element web page together with the mannequin’s capabilities, and implementation tips, you may straight deploy the mannequin by offering an endpoint identify, selecting the variety of cases, and deciding on an occasion sort.
It’s also possible to configure superior choices that allow you to customise the safety and infrastructure settings for the DeepSeek-R1 mannequin together with VPC networking, service function permissions, and encryption settings. For manufacturing deployments, you must evaluate these settings to align together with your group’s safety and compliance necessities.
With Amazon Bedrock Guardrails, you may independently consider person inputs and mannequin outputs. You may 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 purposes. 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 will also be built-in with different Bedrock instruments together with Amazon Bedrock Brokers and Amazon Bedrock Information Bases to construct safer and safer generative AI purposes 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｜>.
Discuss with this step-by-step information on the best way 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 just some clicks. To deploy DeepSeek-R1 in SageMaker JumpStart, you may uncover the DeepSeek-R1 mannequin in SageMaker Unified Studio, SageMaker Studio, SageMaker AI console, or programmatically via 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 may choose the mannequin and select deploy to create an endpoint with default settings. When the endpoint comes InService, you may make inferences by sending requests to its endpoint.
You may derive mannequin efficiency and ML operations controls with Amazon SageMaker AI options resembling Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The mannequin is deployed in an AWS safe surroundings and below your digital personal cloud (VPC) controls, serving to to help knowledge safety.
As like Bedrock Marketpalce, you should utilize the ApplyGuardrail API within the SageMaker JumpStart to decouple safeguards to your generative AI purposes from the DeepSeek-R1 mannequin. Now you can use guardrails with out invoking FMs, which opens the door to extra integration of standardized and completely examined enterprise safeguards to your utility circulate whatever the fashions used.
Discuss with this step-by-step information on the best way 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 supplies the flexibility to import and use your custom-made fashions alongside current FMs via a single serverless, unified API with out the necessity to handle underlying infrastructure. With Amazon Bedrock Customized Mannequin Import, you may import DeepSeek-R1-Distill Llama fashions starting from 1.5–70 billion parameters. As I highlighted in my weblog put up about Amazon Bedrock Mannequin Distillation, the distillation course of entails coaching smaller, extra environment friendly fashions to imitate the conduct 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 below Basis fashions within the Amazon Bedrock console and import and deploy them in a completely managed and serverless surroundings via Amazon Bedrock. This serverless strategy eliminates the necessity for infrastructure administration whereas offering enterprise-grade safety and scalability.
Discuss with this step-by-step information on the best way 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) supplies custom-made machine photos that you should utilize 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 may deploy the DeepSeek-R1-Distill fashions on AWS Trainuim1 or AWS Inferentia2 cases to get the perfect 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 you have linked to your launched ec2 occasion, set up vLLM, an open-source software to serve Massive Language Fashions (LLMs) and obtain the DeepSeek-R1-Distill mannequin from Hugging Face. You may deploy the mannequin utilizing vLLM and invoke the mannequin server.
To be taught extra, confer with this step-by-step information on the best way to deploy DeepSeek-R1-Distill Llama fashions on AWS Inferentia and Trainium.
It’s also possible to 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, numerous guides of its deployment for Amazon EC2 and Amazon Elastic Kubernetes Service (Amazon EKS) have been posted. Right here is a few extra materials so that you can try:
Issues to know
Listed below are a number of necessary issues to know.
- Pricing – For publicly out there fashions like DeepSeek-R1, you might be charged solely the infrastructure value 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 lively, billed in 5-minute home windows. To be taught extra, try the Amazon Bedrock Pricing, Amazon SageMaker AI Pricing, and Amazon EC2 Pricing pages.
- Knowledge safety – You should use enterprise-grade security measures in Amazon Bedrock and Amazon SageMaker that will help you make your knowledge and purposes 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 mostly out there at this time in Amazon Bedrock Market and Amazon SageMaker JumpStart. It’s also possible to 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 attempt at this time within the Amazon Bedrock console, Amazon SageMaker AI console, and Amazon EC2 console, and ship suggestions to AWS re:Submit for Amazon Bedrock and AWS re:Submit for SageMaker AI or via your common AWS Help contacts.
— Channy
