DeepSeek R1 is now available on Nvidia, AWS, and Github
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Published on: 2025-02-04 00:34:00
Source: Latest from TechRadar US in News,opinion
Introduction
- Nvidia integrates DeepSeek-R1 as a NIM microservice
- AWS supports DeepSeek-R1 with a focus on scalable and cost-efficient AI deployment
- Microsoft also has future local deployment plans for DeepSeek
Having taken the AI world by storm in recent weeks, DeepSeek has now made significant strides in expanding the accessibility of its advanced reasoning models.
The company has announced its flagship DeepSeek R1 model is now available on multiple platforms, including Nvidia, AWS, and GitHub.
DeepSeek’s open source nature allows developers to build models based on its architecture, and, at press time, there are 3,374 DeepSeek-based models available collaborative AI-model development platform Hugging Face.
Nvidia, AWS, Github & Azure now offers DeepSeek
On AWS, DeepSeek-R1 models are now accessible through Amazon Bedrock which simplifies API integration and Amazon SageMaker which enables advanced customization and training, supported by AWS Trainium and Inferentia for optimized cost efficiency.
AWS also offers DeepSeek-R1-Distill, a lighter version, through Amazon Bedrock Custom Model Import. This serverless deployment simplifies infrastructure management while maintaining scalability.
Nvidia has also integrated DeepSeek-R1 as a NIM microservice, leveraging its Hopper architecture and FP8 Transformer Engine acceleration to deliver real-time, high-quality responses.
The model, which features 671 billion parameters and a 128,000-token context length, utilizes test-time scaling for improved accuracy.
It also benefits from Nvidia’s Hopper architecture, using FP8 Transformer Engine acceleration and NVLink connectivity. Running on an HGX H200 system, DeepSeek-R1 can generate up to 3,872 tokens per second.
Microsoft’s Azure AI Foundry and GitHub have further expanded DeepSeek’s reach, offering developers a secure and scalable platform to integrate AI into their workflows.
Microsoft has also implemented extensive safety measures, including content filtering and automated assessments. The company claims that it plans to offer distilled versions of DeepSeek-R1 for local deployment on Copilot+ PCs in the future.
DeepSeek-R1 took the world by storm by offering a powerful, cost-efficient AI model with advanced reasoning capabilities and has dethroned popular AI models like ChatGPT.
R1 was reportedly trained for just $6 million, with its most advanced versions being about 95% cheaper to train than comparable models from NVIDIA and Microsoft.
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Top Features
- Nvidia integrates DeepSeek-R1 as a NIM microservice
- AWS supports DeepSeek-R1 with a focus on scalable and cost-efficient AI deployment
- Microsoft also has future local deployment plans for DeepSeek
Having taken the AI world by storm in recent weeks, DeepSeek has now made significant strides in expanding the accessibility of its advanced reasoning models.
The company has announced its flagship DeepSeek R1 model is now available on multiple platforms, including Nvidia, AWS, and GitHub.
DeepSeek’s open source nature allows developers to build models based on its architecture, and, at press time, there are 3,374 DeepSeek-based models available collaborative AI-model development platform Hugging Face.
Nvidia, AWS, Github & Azure now offers DeepSeek
On AWS, DeepSeek-R1 models are now accessible through Amazon Bedrock which simplifies API integration and Amazon SageMaker which enables advanced customization and training, supported by AWS Trainium and Inferentia for optimized cost efficiency.
AWS also offers DeepSeek-R1-Distill, a lighter version, through Amazon Bedrock Custom Model Import. This serverless deployment simplifies infrastructure management while maintaining scalability.
Nvidia has also integrated DeepSeek-R1 as a NIM microservice, leveraging its Hopper architecture and FP8 Transformer Engine acceleration to deliver real-time, high-quality responses.
The model, which features 671 billion parameters and a 128,000-token context length, utilizes test-time scaling for improved accuracy.
It also benefits from Nvidia’s Hopper architecture, using FP8 Transformer Engine acceleration and NVLink connectivity. Running on an HGX H200 system, DeepSeek-R1 can generate up to 3,872 tokens per second.
Microsoft’s Azure AI Foundry and GitHub have further expanded DeepSeek’s reach, offering developers a secure and scalable platform to integrate AI into their workflows.
Microsoft has also implemented extensive safety measures, including content filtering and automated assessments. The company claims that it plans to offer distilled versions of DeepSeek-R1 for local deployment on Copilot+ PCs in the future.
DeepSeek-R1 took the world by storm by offering a powerful, cost-efficient AI model with advanced reasoning capabilities and has dethroned popular AI models like ChatGPT.
R1 was reportedly trained for just $6 million, with its most advanced versions being about 95% cheaper to train than comparable models from NVIDIA and Microsoft.
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Pros and Cons
- Nvidia integrates DeepSeek-R1 as a NIM microservice
- AWS supports DeepSeek-R1 with a focus on scalable and cost-efficient AI deployment
- Microsoft also has future local deployment plans for DeepSeek
Having taken the AI world by storm in recent weeks, DeepSeek has now made significant strides in expanding the accessibility of its advanced reasoning models.
The company has announced its flagship DeepSeek R1 model is now available on multiple platforms, including Nvidia, AWS, and GitHub.
DeepSeek’s open source nature allows developers to build models based on its architecture, and, at press time, there are 3,374 DeepSeek-based models available collaborative AI-model development platform Hugging Face.
Nvidia, AWS, Github & Azure now offers DeepSeek
On AWS, DeepSeek-R1 models are now accessible through Amazon Bedrock which simplifies API integration and Amazon SageMaker which enables advanced customization and training, supported by AWS Trainium and Inferentia for optimized cost efficiency.
AWS also offers DeepSeek-R1-Distill, a lighter version, through Amazon Bedrock Custom Model Import. This serverless deployment simplifies infrastructure management while maintaining scalability.
Nvidia has also integrated DeepSeek-R1 as a NIM microservice, leveraging its Hopper architecture and FP8 Transformer Engine acceleration to deliver real-time, high-quality responses.
The model, which features 671 billion parameters and a 128,000-token context length, utilizes test-time scaling for improved accuracy.
It also benefits from Nvidia’s Hopper architecture, using FP8 Transformer Engine acceleration and NVLink connectivity. Running on an HGX H200 system, DeepSeek-R1 can generate up to 3,872 tokens per second.
Microsoft’s Azure AI Foundry and GitHub have further expanded DeepSeek’s reach, offering developers a secure and scalable platform to integrate AI into their workflows.
Microsoft has also implemented extensive safety measures, including content filtering and automated assessments. The company claims that it plans to offer distilled versions of DeepSeek-R1 for local deployment on Copilot+ PCs in the future.
DeepSeek-R1 took the world by storm by offering a powerful, cost-efficient AI model with advanced reasoning capabilities and has dethroned popular AI models like ChatGPT.
R1 was reportedly trained for just $6 million, with its most advanced versions being about 95% cheaper to train than comparable models from NVIDIA and Microsoft.
You may also like
User Reviews
- Nvidia integrates DeepSeek-R1 as a NIM microservice
- AWS supports DeepSeek-R1 with a focus on scalable and cost-efficient AI deployment
- Microsoft also has future local deployment plans for DeepSeek
Having taken the AI world by storm in recent weeks, DeepSeek has now made significant strides in expanding the accessibility of its advanced reasoning models.
The company has announced its flagship DeepSeek R1 model is now available on multiple platforms, including Nvidia, AWS, and GitHub.
DeepSeek’s open source nature allows developers to build models based on its architecture, and, at press time, there are 3,374 DeepSeek-based models available collaborative AI-model development platform Hugging Face.
Nvidia, AWS, Github & Azure now offers DeepSeek
On AWS, DeepSeek-R1 models are now accessible through Amazon Bedrock which simplifies API integration and Amazon SageMaker which enables advanced customization and training, supported by AWS Trainium and Inferentia for optimized cost efficiency.
AWS also offers DeepSeek-R1-Distill, a lighter version, through Amazon Bedrock Custom Model Import. This serverless deployment simplifies infrastructure management while maintaining scalability.
Nvidia has also integrated DeepSeek-R1 as a NIM microservice, leveraging its Hopper architecture and FP8 Transformer Engine acceleration to deliver real-time, high-quality responses.
The model, which features 671 billion parameters and a 128,000-token context length, utilizes test-time scaling for improved accuracy.
It also benefits from Nvidia’s Hopper architecture, using FP8 Transformer Engine acceleration and NVLink connectivity. Running on an HGX H200 system, DeepSeek-R1 can generate up to 3,872 tokens per second.
Microsoft’s Azure AI Foundry and GitHub have further expanded DeepSeek’s reach, offering developers a secure and scalable platform to integrate AI into their workflows.
Microsoft has also implemented extensive safety measures, including content filtering and automated assessments. The company claims that it plans to offer distilled versions of DeepSeek-R1 for local deployment on Copilot+ PCs in the future.
DeepSeek-R1 took the world by storm by offering a powerful, cost-efficient AI model with advanced reasoning capabilities and has dethroned popular AI models like ChatGPT.
R1 was reportedly trained for just $6 million, with its most advanced versions being about 95% cheaper to train than comparable models from NVIDIA and Microsoft.
