- NVIDIA INTEGRA DEPEEEK-R1 as a NIM microservice
- AWS admits Deepseek-R1 with an approach to the implementation of scalable and profitable
- Microsoft also has future local implementation plans for Depseek
After having taken the world of assault in recent weeks, Depseek has now made significant advances to expand the accessibility of its advanced reasoning models.
The company has announced that its Deepseek R1 flagship model is now available on multiple platforms, including Nvidia, Aws and Github.
The Open Epseek source nature allows developers to build models based on their architecture and, at the time of the press, there are 3,374 models based on Deepseek available for the collaborative models development platform that embrace the face.
Nvidia, Aws, Github & Azure now offers Deepseek
In AWS, the Depseek-R1 models can now be accessed through Amazon’s mother rock, which simplifies the integration of API and Amazon Sagemaker, which allows advanced customization and training, backed by Aws Training and Inferentia for efficiency for efficiency Optimized profitable.
AWS also offers Deepseek-R1-Distill, a lighter version, through the importation of custom models of Amazon Bedrock. This implementation without server simplifies infrastructure management while maintaining scalability.
Nvidia has also integrated Deepseek-R1 as an NIM microservice, taking advantage of its hopper architecture and the engine transformer motor acceleration to offer real-time and high quality responses.
The model, which has 671 billion parameters and a context length of 128,000 Token, uses the test time scale for greater precision.
It also benefits from the Nvidia hopper architecture, using the engine transformer engine and NVLink connectivity. With an HGX H200 system, Depseek-R1 can generate up to 3,872 tokens per second.
Azure Ai Foundry and Github of Microsoft have further expand the extent of Deepseek, offering developers a safe and scalable platform to integrate AI into their workflows.
Microsoft has also implemented extensive security measures, including content filtering and automated evaluations. The company states that it plans to offer distilled versions of Deepseek-R1 for local implementation in co-pilot+ PCs in the future.
Deepseek-R1 took the world by assault by offering a powerful and profitable model with advanced reasoning capabilities and has destroyed popular models such as Chatgpt.
According to reports, R1 was trained for only $ 6 million, with its most advanced versions that were approximately 95% cheaper to train than comparable Nvidia and Microsoft models.
