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Exafunction aims to reduce AI dev costs by abstracting away hardware

TechCrunch

But they share a common bottleneck: hardware. New techniques and chips designed to accelerate certain aspects of AI system development promise to (and, indeed, already have) cut hardware requirements. Emerging from stealth today, Exafunction is developing a platform to abstract away the complexity of using hardware to train AI systems.

Hardware 243
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Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM

AWS Machine Learning - AI

AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently in a containerized environment. Adjust the following configuration to suit your needs, such as the Amazon EKS version, cluster name, and AWS Region.

AWS 87
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Oracle inks deal with AWS to offer database services

CIO

In continuation of its efforts to help enterprises migrate to the cloud, Oracle said it is partnering with Amazon Web Services (AWS) to offer database services on the latter’s infrastructure. Oracle Database@AWS is expected to be available in preview later in the year with broader availability expected in 2025.

AWS 218
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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

As cluster sizes grow, the likelihood of failure increases due to the number of hardware components involved. Each hardware failure can result in wasted GPU hours and requires valuable engineering time to identify and resolve the issue, making the system prone to downtime that can disrupt progress and delay completion.

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Serving LLMs using vLLM and Amazon EC2 instances with AWS AI chips

AWS Machine Learning - AI

Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance inference and scalability. Deploy vLLM on AWS Trainium and Inferentia EC2 instances In these sections, you will be guided through using vLLM on an AWS Inferentia EC2 instance to deploy Meta’s newest Llama 3.2 You will use inf2.xlarge

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Understanding prompt engineering: Unlock the creative potential of Stability AI models on AWS

AWS Machine Learning - AI

of a red apple Practical settings for optimal results To optimize the performance for these models, several key settings should be adjusted based on user preferences and hardware capabilities. A photo of a (red:1.2) apple A (photorealistic:1.4) (3D render:1.2) Start with 28 denoising steps to balance image quality and generation time.

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It's five grand a day to miss our S3 exit

David Heinemeier Hansson

million/year on AWS S3 at the moment to host files for Basecamp , HEY , and everything else. Pure Storage comes with an S3-compatible API, so no need for CEPH, Minio, or any of the other object storage software solutions you might need, if you were trying to do this exercise on commodity hardware. We're spending just shy of $1.5

Storage 101