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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 90
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Cloud Load Balancing- Facilitating Performance & Efficiency of Cloud Resources

RapidValue

Cloud load balancing is the process of distributing workloads and computing resources within a cloud environment. Cloud load balancing also involves hosting the distribution of workload traffic within the internet. Cloud load balancing also involves hosting the distribution of workload traffic within the internet.

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AWS vs. Azure vs. Google Cloud: Comparing Cloud Platforms

Kaseya

In a public cloud, all of the hardware, software, networking and storage infrastructure is owned and managed by the cloud service provider. In addition, you can also take advantage of the reliability of multiple cloud data centers as well as responsive and customizable load balancing that evolves with your changing demands.

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Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight

AWS Machine Learning - AI

We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies. Its serverless architecture allowed the team to rapidly prototype and refine their application without the burden of managing complex hardware infrastructure.

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AWS Disaster Recovery Strategies – PoC with Terraform

Xebia

A regional failure is an uncommon event in AWS (and other Public Cloud providers), where all Availability Zones (AZs) within a region are affected by any condition that impedes the correct functioning of the provisioned Cloud infrastructure. For demonstration purposes, we are using HTTP instead of HTTPS. Pilot Light strategy diagram.

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Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

AWS Machine Learning - AI

DTYPE : This parameter sets the data type for the model weights during loading, with options like float16 or bfloat16 , influencing the models memory consumption and computational performance. There are additional optional runtime parameters that are already pre-optimized in TGI containers to maximize performance on host hardware.

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Host concurrent LLMs with LoRAX

AWS Machine Learning - AI

Traditional model serving approaches can become unwieldy and resource-intensive, leading to increased infrastructure costs, operational overhead, and potential performance bottlenecks, due to the size and hardware requirements to maintain a high-performing FM. Why LoRAX for LoRA deployment on AWS?